2015
Xu, R.; Takeda, F.; Krewer, G.; Li, C.
Measure of mechanical impacts in commercial blueberry packing lines and potential damage to blueberry fruit Journal Article
In: Postharvest Biology and Technology, 110, 103-113, 2015.
Abstract | Links | BibTeX | Tags:
@article{Xu2015,
title = {Measure of mechanical impacts in commercial blueberry packing lines and potential damage to blueberry fruit},
author = {R. Xu and F. Takeda and G. Krewer and C. Li},
url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Measure-of-mechanical-impacts-in-commercial-blueberry-packing-lines-and-potential-damage-to-blueberry-fruit-.pdf},
doi = {10.1016/j.postharvbio.2015.07.013},
year = {2015},
date = {2015-07-15},
journal = {Postharvest Biology and Technology, 110, 103-113},
abstract = {Xu, R., Takeda, F., Krewer, G., & Li, C. (2015). Measure of mechanical impacts in commercial blueberry packing lines and potential damage to blueberry fruit. Postharvest Biology and Technology, 110, 103-113.
Blueberry fruit is susceptible to bruising from mechanical impact. Bruised fruit has shorter postharvest shelf life and softens rapidly in cold storage than non-bruised fruit. A blueberry packing line consists of a hopper for transferring fruit in field containers onto a conveyor line that moves fruit into trash removal equipment, electronic sorter, inspection line, and finally onto clamshell-filling equipment. Blueberry fruit drops as it is transferred from one equipment to the next on the packing line. The mechanical impacts that occur on blueberry packing line equipment were measured quantitatively with a miniature, instrumented sphere called the blueberry impact recording device (BIRD) at 11 packing houses in the United States in 2013 and 2014. The BIRD sensor recorded impacts at transfer points or wherever there was a vertical drop on the packing line. The potential for impact damage was determined in four cultivars (‘Farthing’, ‘O’Neal’, ‘Reveille’ and ‘Star’) by dropping fruit from different heights. The measured data revealed that the largest impacts (∼230 g) were recorded when the sensor dropped into the hopper above the clamshell filler on eight empty lines. The cumulative peakG data showed strong correlation with overall drop height, indicating that reducing the overall drop height on a packing line could reduce the impact level. When the transfer points were padded with Poron foam sheet, significantly lower levels of impact were recorded by the sensor. The BIRD sensor also recorded lower impacts when it was run with fruit on the packing line. The severity of bruise damage resulting from fruit being dropped was related to the impact data recorded by the BIRD sensor. Using peakG-velocity change plot and the fruit bruising rate, several large impacts sufficient to cause bruising were identified, (e.g., >20% of cut surface area indicating bruise damage in 76% of ‘Reveille’ fruit). This paper quantitatively measured the mechanical impact on blueberry packing lines for the first time and the information will assist in improving the design and configuration of blueberry packing line equipment. These changes should result in reducing the magnitude and frequency of mechanical impacts and bruise damage in blueberry fruit.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Xu, R., Takeda, F., Krewer, G., & Li, C. (2015). Measure of mechanical impacts in commercial blueberry packing lines and potential damage to blueberry fruit. Postharvest Biology and Technology, 110, 103-113.
Blueberry fruit is susceptible to bruising from mechanical impact. Bruised fruit has shorter postharvest shelf life and softens rapidly in cold storage than non-bruised fruit. A blueberry packing line consists of a hopper for transferring fruit in field containers onto a conveyor line that moves fruit into trash removal equipment, electronic sorter, inspection line, and finally onto clamshell-filling equipment. Blueberry fruit drops as it is transferred from one equipment to the next on the packing line. The mechanical impacts that occur on blueberry packing line equipment were measured quantitatively with a miniature, instrumented sphere called the blueberry impact recording device (BIRD) at 11 packing houses in the United States in 2013 and 2014. The BIRD sensor recorded impacts at transfer points or wherever there was a vertical drop on the packing line. The potential for impact damage was determined in four cultivars (‘Farthing’, ‘O’Neal’, ‘Reveille’ and ‘Star’) by dropping fruit from different heights. The measured data revealed that the largest impacts (∼230 g) were recorded when the sensor dropped into the hopper above the clamshell filler on eight empty lines. The cumulative peakG data showed strong correlation with overall drop height, indicating that reducing the overall drop height on a packing line could reduce the impact level. When the transfer points were padded with Poron foam sheet, significantly lower levels of impact were recorded by the sensor. The BIRD sensor also recorded lower impacts when it was run with fruit on the packing line. The severity of bruise damage resulting from fruit being dropped was related to the impact data recorded by the BIRD sensor. Using peakG-velocity change plot and the fruit bruising rate, several large impacts sufficient to cause bruising were identified, (e.g., >20% of cut surface area indicating bruise damage in 76% of ‘Reveille’ fruit). This paper quantitatively measured the mechanical impact on blueberry packing lines for the first time and the information will assist in improving the design and configuration of blueberry packing line equipment. These changes should result in reducing the magnitude and frequency of mechanical impacts and bruise damage in blueberry fruit.
Blueberry fruit is susceptible to bruising from mechanical impact. Bruised fruit has shorter postharvest shelf life and softens rapidly in cold storage than non-bruised fruit. A blueberry packing line consists of a hopper for transferring fruit in field containers onto a conveyor line that moves fruit into trash removal equipment, electronic sorter, inspection line, and finally onto clamshell-filling equipment. Blueberry fruit drops as it is transferred from one equipment to the next on the packing line. The mechanical impacts that occur on blueberry packing line equipment were measured quantitatively with a miniature, instrumented sphere called the blueberry impact recording device (BIRD) at 11 packing houses in the United States in 2013 and 2014. The BIRD sensor recorded impacts at transfer points or wherever there was a vertical drop on the packing line. The potential for impact damage was determined in four cultivars (‘Farthing’, ‘O’Neal’, ‘Reveille’ and ‘Star’) by dropping fruit from different heights. The measured data revealed that the largest impacts (∼230 g) were recorded when the sensor dropped into the hopper above the clamshell filler on eight empty lines. The cumulative peakG data showed strong correlation with overall drop height, indicating that reducing the overall drop height on a packing line could reduce the impact level. When the transfer points were padded with Poron foam sheet, significantly lower levels of impact were recorded by the sensor. The BIRD sensor also recorded lower impacts when it was run with fruit on the packing line. The severity of bruise damage resulting from fruit being dropped was related to the impact data recorded by the BIRD sensor. Using peakG-velocity change plot and the fruit bruising rate, several large impacts sufficient to cause bruising were identified, (e.g., >20% of cut surface area indicating bruise damage in 76% of ‘Reveille’ fruit). This paper quantitatively measured the mechanical impact on blueberry packing lines for the first time and the information will assist in improving the design and configuration of blueberry packing line equipment. These changes should result in reducing the magnitude and frequency of mechanical impacts and bruise damage in blueberry fruit.
Wang, W.; Li, C.
A multimodal machine vision system for quality inspection of onions Journal Article
In: Journal of Food Engineering, 166, 291-301, 2015.
Abstract | Links | BibTeX | Tags: hyperspectral
@article{Wang2015,
title = {A multimodal machine vision system for quality inspection of onions},
author = {W. Wang and C. Li},
url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/A-multimodal-machine-vision-system-for-quality-inspection-of-onions-.pdf},
doi = {10.1016/j.jfoodeng.2015.06.027},
year = {2015},
date = {2015-06-18},
urldate = {2015-06-18},
journal = {Journal of Food Engineering, 166, 291-301},
abstract = {Wang, W., & Li, C. (2015). A multimodal machine vision system for quality inspection of onions. Journal of Food Engineering, 166, 291-301.
A multimodal machine vision system was developed to evaluate quality factors of onions holistically and nondestructively. The system integrated hyperspectral, 3D, and X-ray imaging sensors. A LabVIEW program was developed to acquire color images, spectral images, depth images, X-ray images of onions, and measure the weight of onions. With the multimodal data collected, algorithms were developed to calculate the maximum diameter, volume, density, and detect latent defects of onions. Three groups of sweet onions (regular, inoculated with Burkholderia cepacia, and inoculated with Pseudomonas viridiflava) were tested. Results showed that the system accurately measured the weight (RMSE = 3.6 g), diameter (RMSE = 1.7 mm), volume (RMSE = 16.5 cm3), and density (RMSE = 0.03 g/cm3) of onions, and correctly classified 88.9% healthy and defective onions. This work demonstrated a promising approach to evaluate both external and internal quality parameters of onions, which is applicable to onion packinghouses. The proposed system and methods are also potentially applicable to quality inspection of other agricultural products.},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
Wang, W., & Li, C. (2015). A multimodal machine vision system for quality inspection of onions. Journal of Food Engineering, 166, 291-301.
A multimodal machine vision system was developed to evaluate quality factors of onions holistically and nondestructively. The system integrated hyperspectral, 3D, and X-ray imaging sensors. A LabVIEW program was developed to acquire color images, spectral images, depth images, X-ray images of onions, and measure the weight of onions. With the multimodal data collected, algorithms were developed to calculate the maximum diameter, volume, density, and detect latent defects of onions. Three groups of sweet onions (regular, inoculated with Burkholderia cepacia, and inoculated with Pseudomonas viridiflava) were tested. Results showed that the system accurately measured the weight (RMSE = 3.6 g), diameter (RMSE = 1.7 mm), volume (RMSE = 16.5 cm3), and density (RMSE = 0.03 g/cm3) of onions, and correctly classified 88.9% healthy and defective onions. This work demonstrated a promising approach to evaluate both external and internal quality parameters of onions, which is applicable to onion packinghouses. The proposed system and methods are also potentially applicable to quality inspection of other agricultural products.
A multimodal machine vision system was developed to evaluate quality factors of onions holistically and nondestructively. The system integrated hyperspectral, 3D, and X-ray imaging sensors. A LabVIEW program was developed to acquire color images, spectral images, depth images, X-ray images of onions, and measure the weight of onions. With the multimodal data collected, algorithms were developed to calculate the maximum diameter, volume, density, and detect latent defects of onions. Three groups of sweet onions (regular, inoculated with Burkholderia cepacia, and inoculated with Pseudomonas viridiflava) were tested. Results showed that the system accurately measured the weight (RMSE = 3.6 g), diameter (RMSE = 1.7 mm), volume (RMSE = 16.5 cm3), and density (RMSE = 0.03 g/cm3) of onions, and correctly classified 88.9% healthy and defective onions. This work demonstrated a promising approach to evaluate both external and internal quality parameters of onions, which is applicable to onion packinghouses. The proposed system and methods are also potentially applicable to quality inspection of other agricultural products.
Konduru, T.; Rains, G.; Li, C.
Detecting sour skin infected onions using a customized gas sensor array Journal Article
In: Journal of Food Engineering, 160, 19-27, 2015.
Abstract | Links | BibTeX | Tags: e-nose
@article{Konduru2015,
title = {Detecting sour skin infected onions using a customized gas sensor array},
author = {T. Konduru and G. Rains and C. Li},
doi = {10.1016/j.jfoodeng.2015.03.025},
year = {2015},
date = {2015-03-11},
urldate = {2015-03-11},
journal = {Journal of Food Engineering, 160, 19-27},
abstract = {The overall goal of this study was to test a customized gas sensor array in its ability to detect an important postharvest disease (sour skin) in onions. The sensor array consists of seven metal oxide semiconductor gas sensors and a microcontroller-based automatic data logging system. Three features were extracted from the sensor responses and three baseline correction methods were employed to correct the sensors’ responses. The gas sensor array was tested in two separate experiments with two treatments (control and sour skin). The multivariate data analysis revealed that the “relative response” feature combined with relative baseline correction method provided the best discrimination of infected onions among healthy ones. The best performance (85%) was achieved by using the support vector machine model when the data collected from an independent experiment were used for validation. The study demonstrated the potential of a gas sensor array to detect sour skin-infected onions placed among healthy onions in storage.},
keywords = {e-nose},
pubstate = {published},
tppubtype = {article}
}
The overall goal of this study was to test a customized gas sensor array in its ability to detect an important postharvest disease (sour skin) in onions. The sensor array consists of seven metal oxide semiconductor gas sensors and a microcontroller-based automatic data logging system. Three features were extracted from the sensor responses and three baseline correction methods were employed to correct the sensors’ responses. The gas sensor array was tested in two separate experiments with two treatments (control and sour skin). The multivariate data analysis revealed that the “relative response” feature combined with relative baseline correction method provided the best discrimination of infected onions among healthy ones. The best performance (85%) was achieved by using the support vector machine model when the data collected from an independent experiment were used for validation. The study demonstrated the potential of a gas sensor array to detect sour skin-infected onions placed among healthy onions in storage.
Chugunov, S.; Li, C.
In: Computer Physics Communications, 194, 64-75, 2015.
Abstract | Links | BibTeX | Tags: hyperspectral
@article{Chugunov2015b,
title = {Parallel implementation of inverse adding-doubling and Monte Carlo multi-layered programs for high performance computing systems with shared and distributed memory},
author = {S. Chugunov and C. Li},
doi = {10.1016/j.cpc.2015.02.029},
year = {2015},
date = {2015-02-21},
urldate = {2015-02-21},
journal = {Computer Physics Communications, 194, 64-75},
abstract = {Parallel implementation of two numerical tools popular in optical studies of biological materials–Inverse Adding-Doubling (IAD) program and Monte Carlo Multi-Layered (MCML) program–was developed and tested in this study. The implementation was based on Message Passing Interface (MPI) and standard C-language. Parallel versions of IAD and MCML programs were compared to their sequential counterparts in validation and performance tests. Additionally, the portability of the programs was tested using a local high performance computing (HPC) cluster, Penguin-On-Demand HPC cluster, and Amazon EC2 cluster. Parallel IAD was tested with up to 150 parallel cores using 1223 input datasets. It demonstrated linear scalability and the speedup was proportional to the number of parallel cores (up to 150x). Parallel MCML was tested with up to 1001 parallel cores using problem sizes 104–109 photon packets. It demonstrated classical performance curves featuring communication overhead and performance saturation point. Optimal performance curve was derived for parallel MCML as a function of problem size. Typical speedup achieved for parallel MCML (up to 326x) demonstrated linear increase with problem size. Precision of MCML results was estimated in a series of tests - problem size of 106 photon packets was found optimal for calculations of total optical response and 108 photon packets for spatially-resolved results. The presented parallel versions of MCML and IAD programs are portable on multiple computing platforms. The parallel programs could significantly speed up the simulation for scientists and be utilized to their full potential in computing systems that are readily available without additional costs. },
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
Parallel implementation of two numerical tools popular in optical studies of biological materials–Inverse Adding-Doubling (IAD) program and Monte Carlo Multi-Layered (MCML) program–was developed and tested in this study. The implementation was based on Message Passing Interface (MPI) and standard C-language. Parallel versions of IAD and MCML programs were compared to their sequential counterparts in validation and performance tests. Additionally, the portability of the programs was tested using a local high performance computing (HPC) cluster, Penguin-On-Demand HPC cluster, and Amazon EC2 cluster. Parallel IAD was tested with up to 150 parallel cores using 1223 input datasets. It demonstrated linear scalability and the speedup was proportional to the number of parallel cores (up to 150x). Parallel MCML was tested with up to 1001 parallel cores using problem sizes 104–109 photon packets. It demonstrated classical performance curves featuring communication overhead and performance saturation point. Optimal performance curve was derived for parallel MCML as a function of problem size. Typical speedup achieved for parallel MCML (up to 326x) demonstrated linear increase with problem size. Precision of MCML results was estimated in a series of tests - problem size of 106 photon packets was found optimal for calculations of total optical response and 108 photon packets for spatially-resolved results. The presented parallel versions of MCML and IAD programs are portable on multiple computing platforms. The parallel programs could significantly speed up the simulation for scientists and be utilized to their full potential in computing systems that are readily available without additional costs.
Jiang, Y.; Li, C.
Detection and Discrimination of Cotton Foreign Matter Using Push-Broom Based Hyperspectral Imaging: System Design and Capability Journal Article
In: PLoS ONE , 10(3), e0121969, 2015.
Abstract | Links | BibTeX | Tags: hyperspectral
@article{Y2015,
title = {Detection and Discrimination of Cotton Foreign Matter Using Push-Broom Based Hyperspectral Imaging: System Design and Capability},
author = {Y. Jiang and C. Li},
doi = {10.1371/journal.pone.0121969},
year = {2015},
date = {2015-02-06},
urldate = {2015-02-06},
journal = {PLoS ONE , 10(3), e0121969},
abstract = {Cotton quality, a major factor determining both cotton profitability and marketability, is affected by not only the overall quantity of but also the type of the foreign matter. Although current commercial instruments can measure the overall amount of the foreign matter, no instrument can differentiate various types of foreign matter. The goal of this study was to develop a hyperspectral imaging system to discriminate major types of foreign matter in cotton lint. A push-broom based hyperspectral imaging system with a custom-built multi-thread software was developed to acquire hyperspectral images of cotton fiber with 15 types of foreign matter commonly found in the U.S. cotton lint. A total of 450 (30 replicates for each foreign matter) foreign matter samples were cut into 1 by 1 cm2 pieces and imaged on the lint surface using reflectance mode in the spectral range from 400-1000 nm. The mean spectra of the foreign matter and lint were extracted from the user-defined region-of-interests in the hyperspectral images. The principal component analysis was performed on the mean spectra to reduce the feature dimension from the original 256 bands to the top 3 principal components. The score plots of the 3 principal components were used to examine clusterization patterns for classifying the foreign matter. These patterns were further validated by statistical tests. The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint. Nine types of cotton foreign matter formed distinct clusters in the score plots. Additionally, all of them were significantly different from each other at the significance level of 0.05 except brown leaf and bract. The developed hyperspectral imaging system is effective to detect and classify cotton foreign matter on the lint surface and has the potential to be implemented in commercial cotton classing offices.},
keywords = {hyperspectral},
pubstate = {published},
tppubtype = {article}
}
Cotton quality, a major factor determining both cotton profitability and marketability, is affected by not only the overall quantity of but also the type of the foreign matter. Although current commercial instruments can measure the overall amount of the foreign matter, no instrument can differentiate various types of foreign matter. The goal of this study was to develop a hyperspectral imaging system to discriminate major types of foreign matter in cotton lint. A push-broom based hyperspectral imaging system with a custom-built multi-thread software was developed to acquire hyperspectral images of cotton fiber with 15 types of foreign matter commonly found in the U.S. cotton lint. A total of 450 (30 replicates for each foreign matter) foreign matter samples were cut into 1 by 1 cm2 pieces and imaged on the lint surface using reflectance mode in the spectral range from 400-1000 nm. The mean spectra of the foreign matter and lint were extracted from the user-defined region-of-interests in the hyperspectral images. The principal component analysis was performed on the mean spectra to reduce the feature dimension from the original 256 bands to the top 3 principal components. The score plots of the 3 principal components were used to examine clusterization patterns for classifying the foreign matter. These patterns were further validated by statistical tests. The experimental results showed that the mean spectra of all 15 types of cotton foreign matter were different from that of the lint. Nine types of cotton foreign matter formed distinct clusters in the score plots. Additionally, all of them were significantly different from each other at the significance level of 0.05 except brown leaf and bract. The developed hyperspectral imaging system is effective to detect and classify cotton foreign matter on the lint surface and has the potential to be implemented in commercial cotton classing offices.
Xu, R.; Li, C.
Development of the Second Generation Berry Impact Recording Device (BIRD II) Journal Article
In: Sensors, 15(2), 3688-3705, 2015.
Abstract | Links | BibTeX | Tags:
@article{Xu2015b,
title = {Development of the Second Generation Berry Impact Recording Device (BIRD II)},
author = {R. Xu and C. Li},
doi = {10.3390/s150203688},
year = {2015},
date = {2015-02-05},
journal = {Sensors, 15(2), 3688-3705},
abstract = {To quantitatively measure the impacts during blueberry harvesting and post-harvest handling, this study designed the second generation Berry Impact Recording Device (BIRD II) sensor with a size of 21 mm in diameter and a weight of 3.9 g, which reduced the size by 17% and the weight by 50% compared to the previous prototype. The sensor was able to measure accelerations up to 346 g at a maximum frequency of 2 KHz. Universal Serial Bus (USB) was used to directly connect the sensor with the computer, removing the interface box used previously. LabVIEW-based PC software was designed to configure the sensor, download and process the data. The sensor was calibrated using a centrifuge. The accuracy of the sensor was between -1.76 g to 2.17 g, and the precision was between 0.21 g to 0.81 g. Dynamic drop tests showed that BIRD II had smaller variance in measurements than BIRD I. In terms of size and weight, BIRD II is more similar to an average blueberry fruit than BIRD I, which leads to more accurate measurements of the impacts for blueberries.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To quantitatively measure the impacts during blueberry harvesting and post-harvest handling, this study designed the second generation Berry Impact Recording Device (BIRD II) sensor with a size of 21 mm in diameter and a weight of 3.9 g, which reduced the size by 17% and the weight by 50% compared to the previous prototype. The sensor was able to measure accelerations up to 346 g at a maximum frequency of 2 KHz. Universal Serial Bus (USB) was used to directly connect the sensor with the computer, removing the interface box used previously. LabVIEW-based PC software was designed to configure the sensor, download and process the data. The sensor was calibrated using a centrifuge. The accuracy of the sensor was between -1.76 g to 2.17 g, and the precision was between 0.21 g to 0.81 g. Dynamic drop tests showed that BIRD II had smaller variance in measurements than BIRD I. In terms of size and weight, BIRD II is more similar to an average blueberry fruit than BIRD I, which leads to more accurate measurements of the impacts for blueberries.
Konduru, T.; Rains, G.; Li, C.
A customized metal oxide semiconductor-based gas sensor array for onion quality evaluation: system development and characterization Journal Article
In: Sensors, 15(1), 1252-1273, 2015.
Abstract | Links | BibTeX | Tags: e-nose
@article{Konduru2015b,
title = {A customized metal oxide semiconductor-based gas sensor array for onion quality evaluation: system development and characterization},
author = {T. Konduru and G. Rains and C. Li},
doi = {10.3390/s150101252},
year = {2015},
date = {2015-01-04},
urldate = {2015-01-04},
journal = {Sensors, 15(1), 1252-1273},
abstract = {A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped with a gas delivery system to pump volatiles from the onion samples into the chamber. The electronic circuit mainly comprised a microcontroller, non-volatile memory chip, and trickle-charge real time clock chip, serial communication chip, and parallel LCD panel. User preferences are communicated with the on-board microcontroller through a graphical user interface developed using LabVIEW. The developed gas sensor array was characterized and the discrimination potential was tested by exposing it to three different concentrations of acetone (ketone), acetonitrile (nitrile), ethyl acetate (ester), and ethanol (alcohol). The gas sensor array could differentiate the four chemicals of same concentrations and different concentrations within the chemical with significant difference. Experiment results also showed that the system was able to discriminate two concentrations (196 and 1964 ppm) of methlypropyl sulfide and two concentrations (145 and 1452 ppm) of 2-nonanone, two key volatile compounds emitted by rotten onions. As a proof of concept, the gas sensor array was able to achieve 89% correct classification of sour skin infected onions. The customized low-cost gas sensor array could be a useful tool to detect onion postharvest diseases in storage.},
keywords = {e-nose},
pubstate = {published},
tppubtype = {article}
}
A gas sensor array, consisting of seven Metal Oxide Semiconductor (MOS) sensors that are sensitive to a wide range of organic volatile compounds was developed to detect rotten onions during storage. These MOS sensors were enclosed in a specially designed Teflon chamber equipped with a gas delivery system to pump volatiles from the onion samples into the chamber. The electronic circuit mainly comprised a microcontroller, non-volatile memory chip, and trickle-charge real time clock chip, serial communication chip, and parallel LCD panel. User preferences are communicated with the on-board microcontroller through a graphical user interface developed using LabVIEW. The developed gas sensor array was characterized and the discrimination potential was tested by exposing it to three different concentrations of acetone (ketone), acetonitrile (nitrile), ethyl acetate (ester), and ethanol (alcohol). The gas sensor array could differentiate the four chemicals of same concentrations and different concentrations within the chemical with significant difference. Experiment results also showed that the system was able to discriminate two concentrations (196 and 1964 ppm) of methlypropyl sulfide and two concentrations (145 and 1452 ppm) of 2-nonanone, two key volatile compounds emitted by rotten onions. As a proof of concept, the gas sensor array was able to achieve 89% correct classification of sour skin infected onions. The customized low-cost gas sensor array could be a useful tool to detect onion postharvest diseases in storage.
2014
Mustafic, A.; Li, C.; Haidekker, M.
Blue and UV LED-induced fluorescence in cotton foreign matter Journal Article
In: Journal of Biological Engineering, 8(1), 29, 2014.
Abstract | Links | BibTeX | Tags:
@article{Mustafic2014,
title = {Blue and UV LED-induced fluorescence in cotton foreign matter},
author = {A. Mustafic and C. Li and M. Haidekker},
doi = {10.1186/1754-1611-8-29},
year = {2014},
date = {2014-10-15},
journal = {Journal of Biological Engineering, 8(1), 29},
abstract = {Cotton is an important domesticated fiber used to manufacture a variety of products and industrial goods. During harvesting with cotton strippers and cotton pickers, it is contaminated with foreign matter from botanical and non-botanical sources which adversely affect the quality and consistency of cotton, and therefore reduces its market value. To improve the current grading done by the High Volume Instrument (HVI) and human inspectors, it was explored whether fluorescence imaging can be used for cotton foreign matter detection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cotton is an important domesticated fiber used to manufacture a variety of products and industrial goods. During harvesting with cotton strippers and cotton pickers, it is contaminated with foreign matter from botanical and non-botanical sources which adversely affect the quality and consistency of cotton, and therefore reduces its market value. To improve the current grading done by the High Volume Instrument (HVI) and human inspectors, it was explored whether fluorescence imaging can be used for cotton foreign matter detection.
Wang, W.; Li, C.
Optical Properties of Healthy and Diseased Onion Tissues in the Visible and Near-Infrared Spectral Region Journal Article
In: Transactions of ASABE, 57(6), 1771-1782, 2014.
Abstract | Links | BibTeX | Tags:
@article{Wang2014,
title = {Optical Properties of Healthy and Diseased Onion Tissues in the Visible and Near-Infrared Spectral Region},
author = {W. Wang and C. Li},
doi = {10.13031/trans.57.10815},
year = {2014},
date = {2014-08-06},
journal = {Transactions of ASABE, 57(6), 1771-1782},
abstract = {Optical techniques such as spectroscopy and hyperspectral imaging are promising tools for the nondestructive inspection of onions. To apply optical techniques on onions appropriately, it is important to understand the fundamental optical properties of onion tissues. In this study, the light absorption coefficient (μa), reduced scattering coefficient (μs′), and scattering anisotropy (g) of onion tissues were estimated in the wavelength ranges of 550-880 nm and 950-1650 nm. Dry skin and flesh samples of healthy onions, Burkholderia cepacia-infected (causing sour skin) onions, and Botrytis aclada-infected (causing neck rot) onions were tested. The total diffuse reflectance, total transmittance, and collimated transmittance spectra of the samples were collected using spectroscopic systems that consisted of an integrating sphere, fiber optic guide, and spectrometer. Based on the collected spectra, the μa, μs′, and g values of the onion tissues were calculated using the inverse adding-doubling method. The results indicated that onion dry skin and flesh were scattering-dominated biological tissues at wavelengths below 1300 nm. The μa values of the dry skin of onions with sour skin were significantly greater than those of healthy onions in the near-infrared region. Dry skin samples from onions with neck rot were statistically different from those of healthy onions in terms of μa in 550-750 nm and μs′ in 550-1650 nm. The flesh of onions with sour skin or neck rot was significantly different from that of healthy onions in terms of μa in 550-1100 nm and μs′ in 550-1650 nm. This study demonstrates the feasibility of detecting diseased onions by investigating their optical characteristics. The measured optical properties of healthy and diseased onion tissues can be used in theoretical modeling and simulations of light-onion interactions for developing onion quality inspection systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Optical techniques such as spectroscopy and hyperspectral imaging are promising tools for the nondestructive inspection of onions. To apply optical techniques on onions appropriately, it is important to understand the fundamental optical properties of onion tissues. In this study, the light absorption coefficient (μa), reduced scattering coefficient (μs′), and scattering anisotropy (g) of onion tissues were estimated in the wavelength ranges of 550-880 nm and 950-1650 nm. Dry skin and flesh samples of healthy onions, Burkholderia cepacia-infected (causing sour skin) onions, and Botrytis aclada-infected (causing neck rot) onions were tested. The total diffuse reflectance, total transmittance, and collimated transmittance spectra of the samples were collected using spectroscopic systems that consisted of an integrating sphere, fiber optic guide, and spectrometer. Based on the collected spectra, the μa, μs′, and g values of the onion tissues were calculated using the inverse adding-doubling method. The results indicated that onion dry skin and flesh were scattering-dominated biological tissues at wavelengths below 1300 nm. The μa values of the dry skin of onions with sour skin were significantly greater than those of healthy onions in the near-infrared region. Dry skin samples from onions with neck rot were statistically different from those of healthy onions in terms of μa in 550-750 nm and μs′ in 550-1650 nm. The flesh of onions with sour skin or neck rot was significantly different from that of healthy onions in terms of μa in 550-1100 nm and μs′ in 550-1650 nm. This study demonstrates the feasibility of detecting diseased onions by investigating their optical characteristics. The measured optical properties of healthy and diseased onion tissues can be used in theoretical modeling and simulations of light-onion interactions for developing onion quality inspection systems.
Mustafic, A.; Li, C.
Classification of cotton foreign matter using color features extracted from fluorescent images Journal Article
In: Textile Research Journal, 85(12), 1209-1220, 2014.
Abstract | Links | BibTeX | Tags:
@article{Mustafic2014b,
title = {Classification of cotton foreign matter using color features extracted from fluorescent images},
author = {A. Mustafic and C. Li},
doi = {10.1177/0040517514561923},
year = {2014},
date = {2014-08-01},
journal = {Textile Research Journal, 85(12), 1209-1220},
abstract = {The presence of foreign matter affects the quality and ultimately the monetary value of cotton lint. Current methods, such as the High Volume Instrument, compute the overall area of the foreign matter, but cannot identify the specific type. A fluorescent imaging system was investigated to classify the six types of botanical and seven types of non-botanical foreign matter under blue and ultraviolet (UV) light-emitting diode (LED) excitation lights, respectively. Two color models (RGB (red, green, blue) and HSV (hue, saturation, value)) were used and ratio images and single-channel images were examined. The F-values from the multivariate analysis of variance were used to select the three most contributing color features from the blue LED (R/G, H, V) and the UV LED dataset (B/G, S, V). The linear discriminant analysis model achieved classification rates of 80% or higher for bract, green leaf, hull, paper, plastic bag, plastic packaging, seed, and stem, and classification rates between 60% and 80% for bark, seed coat, and twine. The study demonstrated that fluorescence imaging is a promising tool to classify major types of cotton foreign matter and could be used for cotton classing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The presence of foreign matter affects the quality and ultimately the monetary value of cotton lint. Current methods, such as the High Volume Instrument, compute the overall area of the foreign matter, but cannot identify the specific type. A fluorescent imaging system was investigated to classify the six types of botanical and seven types of non-botanical foreign matter under blue and ultraviolet (UV) light-emitting diode (LED) excitation lights, respectively. Two color models (RGB (red, green, blue) and HSV (hue, saturation, value)) were used and ratio images and single-channel images were examined. The F-values from the multivariate analysis of variance were used to select the three most contributing color features from the blue LED (R/G, H, V) and the UV LED dataset (B/G, S, V). The linear discriminant analysis model achieved classification rates of 80% or higher for bract, green leaf, hull, paper, plastic bag, plastic packaging, seed, and stem, and classification rates between 60% and 80% for bark, seed coat, and twine. The study demonstrated that fluorescence imaging is a promising tool to classify major types of cotton foreign matter and could be used for cotton classing.
Wang, W.; Li, C.
Size estimation of sweet onions using consumer-grade RGB-depth sensor Journal Article
In: Food Engineering, 142, 153-162, 2014.
Abstract | Links | BibTeX | Tags:
@article{Wang2014b,
title = {Size estimation of sweet onions using consumer-grade RGB-depth sensor},
author = {W. Wang and C. Li},
doi = {10.1016/j.jfoodeng.2014.06.019},
year = {2014},
date = {2014-06-09},
journal = {Food Engineering, 142, 153-162},
abstract = {Size estimation is an important aspect of the postharvest handling of onions. This study applied the RGB-depth (RGB-D) sensor to measure the maximum diameter and volume of sweet onions, and estimated the density of onions using measured parameters. RGB-D images were acquired when onions were placed at six different orientations. The maximum diameter was calculated using both the color and depth images. The volume was estimated using the depth images. The onion diameter estimated by depth images achieved a higher average accuracy and robustness (RMSE = 2 mm) than those calculated by color images (RMSE = 3.4 mm). The predicted volume of onions showed a RMSE of 18.5 cm3 and an accuracy of 96.3%. Results also demonstrated that it is promising to nondestructively estimate the onion density based on its depth image. The proposed methods can be applied to improve the efficacy and efficiency of size estimation in onion phenotyping and postharvest sorting/grading.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Size estimation is an important aspect of the postharvest handling of onions. This study applied the RGB-depth (RGB-D) sensor to measure the maximum diameter and volume of sweet onions, and estimated the density of onions using measured parameters. RGB-D images were acquired when onions were placed at six different orientations. The maximum diameter was calculated using both the color and depth images. The volume was estimated using the depth images. The onion diameter estimated by depth images achieved a higher average accuracy and robustness (RMSE = 2 mm) than those calculated by color images (RMSE = 3.4 mm). The predicted volume of onions showed a RMSE of 18.5 cm3 and an accuracy of 96.3%. Results also demonstrated that it is promising to nondestructively estimate the onion density based on its depth image. The proposed methods can be applied to improve the efficacy and efficiency of size estimation in onion phenotyping and postharvest sorting/grading.
Yu, P.; Li, C.; Takeda, F.; Krewer, G.
Visual bruise assessment and analysis of mechanical impact measurement in southern highbush blueberry Journal Article
In: Applied Engineering in Agriculture, 30(1), 29-37, 2014.
Abstract | Links | BibTeX | Tags:
@article{Yu2014,
title = {Visual bruise assessment and analysis of mechanical impact measurement in southern highbush blueberry},
author = {P. Yu and C. Li and F. Takeda and G. Krewer},
doi = {10.13031/aea.30.10224},
year = {2014},
date = {2014-01-02},
journal = {Applied Engineering in Agriculture, 30(1), 29-37},
abstract = {Blueberries are prone to bruise damages and the majority of the fruit destined for the fresh market is handharvested.
The industry needs to harvest fruit mechanically to reduce harvest cost and improve production efficiency while
maintaining fresh market quality. In this study the bruise susceptibility of three firm-textured and one soft-textured
highbush blueberry genotypes was related with the data recorded by the berry impact recording device (BIRD) by
dropping both the fruit and the BIRD sensor onto two types of contacting surfaces (hard plastic and padding material).
Bruise damages were evaluated by dropping the fruit from certain heights and assessing for tissue discoloration
afterwards. The drop test confirmed that a soft-textured genotype (‘Scintilla’) was more susceptible to bruising (76%
bruise incidence at 120 cm drop height on hard plastic surface) than the firm-textured genotypes (‘Farthing,’ ‘Sweetcrisp,’
and selection FL 05-528) (31-68% bruise incidence under the same drop condition). The selection was proven to be a
promising machine harvestable genotype in terms of the resistance to bruising. The bruise incidences were related to the
impact data recorded by the sensor. Using peak acceleration alone revealed a close relationship between BIRD sensor
measurements and fruit bruising incidence when the sensor and fruit were dropped on hard plastic surfaces, but a close
relationship between bruise incidence and sensor measurement was not established on the padded surface. Using both the
peak acceleration and velocity change, we established bruising zones for each of the four highbush genotypes. This was
useful in translating the impact data recorded by the BIRD sensor into bruising probability of a blueberry genotype. The
sensor and the interpretation method relating fruit damage to BIRD data enable evaluation of various padding materials
and machine designs in terms of the bruise damage they produce in the blueberry fruit.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Blueberries are prone to bruise damages and the majority of the fruit destined for the fresh market is handharvested.
The industry needs to harvest fruit mechanically to reduce harvest cost and improve production efficiency while
maintaining fresh market quality. In this study the bruise susceptibility of three firm-textured and one soft-textured
highbush blueberry genotypes was related with the data recorded by the berry impact recording device (BIRD) by
dropping both the fruit and the BIRD sensor onto two types of contacting surfaces (hard plastic and padding material).
Bruise damages were evaluated by dropping the fruit from certain heights and assessing for tissue discoloration
afterwards. The drop test confirmed that a soft-textured genotype (‘Scintilla’) was more susceptible to bruising (76%
bruise incidence at 120 cm drop height on hard plastic surface) than the firm-textured genotypes (‘Farthing,’ ‘Sweetcrisp,’
and selection FL 05-528) (31-68% bruise incidence under the same drop condition). The selection was proven to be a
promising machine harvestable genotype in terms of the resistance to bruising. The bruise incidences were related to the
impact data recorded by the sensor. Using peak acceleration alone revealed a close relationship between BIRD sensor
measurements and fruit bruising incidence when the sensor and fruit were dropped on hard plastic surfaces, but a close
relationship between bruise incidence and sensor measurement was not established on the padded surface. Using both the
peak acceleration and velocity change, we established bruising zones for each of the four highbush genotypes. This was
useful in translating the impact data recorded by the BIRD sensor into bruising probability of a blueberry genotype. The
sensor and the interpretation method relating fruit damage to BIRD data enable evaluation of various padding materials
and machine designs in terms of the bruise damage they produce in the blueberry fruit.
The industry needs to harvest fruit mechanically to reduce harvest cost and improve production efficiency while
maintaining fresh market quality. In this study the bruise susceptibility of three firm-textured and one soft-textured
highbush blueberry genotypes was related with the data recorded by the berry impact recording device (BIRD) by
dropping both the fruit and the BIRD sensor onto two types of contacting surfaces (hard plastic and padding material).
Bruise damages were evaluated by dropping the fruit from certain heights and assessing for tissue discoloration
afterwards. The drop test confirmed that a soft-textured genotype (‘Scintilla’) was more susceptible to bruising (76%
bruise incidence at 120 cm drop height on hard plastic surface) than the firm-textured genotypes (‘Farthing,’ ‘Sweetcrisp,’
and selection FL 05-528) (31-68% bruise incidence under the same drop condition). The selection was proven to be a
promising machine harvestable genotype in terms of the resistance to bruising. The bruise incidences were related to the
impact data recorded by the sensor. Using peak acceleration alone revealed a close relationship between BIRD sensor
measurements and fruit bruising incidence when the sensor and fruit were dropped on hard plastic surfaces, but a close
relationship between bruise incidence and sensor measurement was not established on the padded surface. Using both the
peak acceleration and velocity change, we established bruising zones for each of the four highbush genotypes. This was
useful in translating the impact data recorded by the BIRD sensor into bruising probability of a blueberry genotype. The
sensor and the interpretation method relating fruit damage to BIRD data enable evaluation of various padding materials
and machine designs in terms of the bruise damage they produce in the blueberry fruit.
2013
Yu, P.; Li, C.; Takeda, F.; Krewer, G.; Rains, G.; Hamrita, T.
Measurement of mechanical impacts created by rotary, slapper, and sway blueberry mechanical harvesters Journal Article
In: Computers and Electronics in Agriculture, 101, 84-92, 2013.
Abstract | Links | BibTeX | Tags:
@article{Yu2013,
title = {Measurement of mechanical impacts created by rotary, slapper, and sway blueberry mechanical harvesters},
author = {P. Yu and C. Li and F. Takeda and G. Krewer and G. Rains and T. Hamrita},
doi = {10.1016/j.compag.2013.12.001},
year = {2013},
date = {2013-10-04},
journal = {Computers and Electronics in Agriculture, 101, 84-92},
abstract = {Blueberry mechanical harvesters cause bruise damage to the fruit. The goal of this study was to test a custom-made sensor (berry impact recording device) to measure the quality and magnitude of mechanical impacts created by three major types of commercial blueberry mechanical harvesters (rotary, slapper, and sway). The sensor was mounted on blueberry bushes (cultivated) and harvested at standard operating conditions such that the sensor was detached and experienced the impact forces typically found during a mechanical harvesting process. The data collected by the sensor revealed that the slapper and sway harvesters generated not only larger number but also higher magnitude impacts than the rotary. Our analyses suggest that these disparities were mostly caused by different agitating mechanisms, contacting surface materials, and designs between the three harvesters. Results indicated that most impacts lasted 5–7 ms in all three harvesters. The distribution of the impacts showed that 90% of impacts from the rotary were less than 190 g and 90% of impacts from the slapper and sway were less than 250 g. Corresponding measures were identified to reduce potential bruise damage in the harvesters. The information could be useful to select harvesters that create the least impacts and to improve current mechanical harvester designs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Blueberry mechanical harvesters cause bruise damage to the fruit. The goal of this study was to test a custom-made sensor (berry impact recording device) to measure the quality and magnitude of mechanical impacts created by three major types of commercial blueberry mechanical harvesters (rotary, slapper, and sway). The sensor was mounted on blueberry bushes (cultivated) and harvested at standard operating conditions such that the sensor was detached and experienced the impact forces typically found during a mechanical harvesting process. The data collected by the sensor revealed that the slapper and sway harvesters generated not only larger number but also higher magnitude impacts than the rotary. Our analyses suggest that these disparities were mostly caused by different agitating mechanisms, contacting surface materials, and designs between the three harvesters. Results indicated that most impacts lasted 5–7 ms in all three harvesters. The distribution of the impacts showed that 90% of impacts from the rotary were less than 190 g and 90% of impacts from the slapper and sway were less than 250 g. Corresponding measures were identified to reduce potential bruise damage in the harvesters. The information could be useful to select harvesters that create the least impacts and to improve current mechanical harvester designs.
Wang, H.; Li, C.; Wang, M.
Quantitative determination of onion internal quality using hyperspectral imaging with reflectance, interactance, and transmittance modes Journal Article
In: Transactions of ASABE, 56(4), 1623-1635, 2013.
Abstract | Links | BibTeX | Tags:
@article{Wang2013,
title = {Quantitative determination of onion internal quality using hyperspectral imaging with reflectance, interactance, and transmittance modes},
author = {H. Wang and C. Li and M. Wang},
doi = {10.13031/trans.56.9883},
year = {2013},
date = {2013-07-26},
journal = {Transactions of ASABE, 56(4), 1623-1635},
abstract = {The internal quality of onions is important to both consumers and onion processors, but current methods for evaluating the internal quality are mostly destructive. The overall goal of this study was to investigate the feasibility of using hyperspectral imaging technology to quantitatively predict the amount of dry matter, the soluble solids content, and the firmness of onions. A total of 308 onions were scanned using a line-scan hyperspectral imaging system with three sensing modes (reflectance, interactance, and transmittance) in the spectral region of 400-1000 nm. An ellipsoidal model was developed to correct the uneven illumination caused by the curvature of the surface in the reflectance images. The spectra extracted from onion spectral images were used to develop partial least squares (PLS) regression models. Results showed that interactance achieved comparable or even better results than transmittance, and the two modes performed significantly better than diffuse reflectance. In interactance mode, soluble solids content [coefficient of determination (R2) = 0.93, standard error of prediction (SEP) = 1.46 °Brix] and dry matter (R2 = 0.93, SEP = 1.61%) can be estimated better than firmness (R2 = 0.59, SEP = 9.75 N). This study demonstrated for the first time that the interactance mode of the hyperspectral imaging technique can be used to quantitatively predict the internal quality properties of an onion. The lab-based hyperspectral imaging system has the potential to be used in an automated online quality inspection system for predicting the internal quality of onions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The internal quality of onions is important to both consumers and onion processors, but current methods for evaluating the internal quality are mostly destructive. The overall goal of this study was to investigate the feasibility of using hyperspectral imaging technology to quantitatively predict the amount of dry matter, the soluble solids content, and the firmness of onions. A total of 308 onions were scanned using a line-scan hyperspectral imaging system with three sensing modes (reflectance, interactance, and transmittance) in the spectral region of 400-1000 nm. An ellipsoidal model was developed to correct the uneven illumination caused by the curvature of the surface in the reflectance images. The spectra extracted from onion spectral images were used to develop partial least squares (PLS) regression models. Results showed that interactance achieved comparable or even better results than transmittance, and the two modes performed significantly better than diffuse reflectance. In interactance mode, soluble solids content [coefficient of determination (R2) = 0.93, standard error of prediction (SEP) = 1.46 °Brix] and dry matter (R2 = 0.93, SEP = 1.61%) can be estimated better than firmness (R2 = 0.59, SEP = 9.75 N). This study demonstrated for the first time that the interactance mode of the hyperspectral imaging technique can be used to quantitatively predict the internal quality properties of an onion. The lab-based hyperspectral imaging system has the potential to be used in an automated online quality inspection system for predicting the internal quality of onions.
Wang, W.; Li, C.
Measurement of the light absorption and scattering properties of onion skin and flesh at 633 nm Journal Article
In: Postharvest Biology and Technology, 86, 494-501, 2013.
Abstract | Links | BibTeX | Tags:
@article{Wang2013b,
title = {Measurement of the light absorption and scattering properties of onion skin and flesh at 633 nm},
author = {W. Wang and C. Li},
doi = {10.1016/j.postharvbio.2013.07.032},
year = {2013},
date = {2013-07-26},
journal = {Postharvest Biology and Technology, 86, 494-501},
abstract = {Understanding the optical properties of onion tissues is essential to applying optical methods for onion quality inspection. This study estimated the optical properties of dry skin, wet skin, and flesh of red, Vidalia sweet, white, and yellow onions at the wavelength of 633 nm. The total diffuse reflectance, total transmittance, and collimated transmittance of single-layer onion tissues were measured by spectroscopic systems. Based on the measured data, the absorption coefficient μa and the reduced scattering coefficient of onion tissues were calculated using the inverse adding-doubling method. The results indicated that the dry and wet skins had significantly higher μa and than the flesh at 633 nm. For both skins and flesh, the μa varied between cultivars, while the differences of the between cultivars were less profound. All types of onion tissues were high-albedo materials at 633 nm. Using the calculated optical properties, Monte Carlo simulations were performed to model the light propagation in 25 different scenarios of multi-layer onion tissues for four cultivars, respectively. The results showed that the incident light at 633 nm would lose 99% of its energy within 6 layers in any of the simulated scenarios, and the light penetrated more layers in the sweet onions than in the other three cultivars. This work provided fundamental understanding of the optical properties of onion tissues and the light propagation in onion bulbs at 633 nm. The investigation of the onion optical properties will be extended to a broader spectrum in the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Understanding the optical properties of onion tissues is essential to applying optical methods for onion quality inspection. This study estimated the optical properties of dry skin, wet skin, and flesh of red, Vidalia sweet, white, and yellow onions at the wavelength of 633 nm. The total diffuse reflectance, total transmittance, and collimated transmittance of single-layer onion tissues were measured by spectroscopic systems. Based on the measured data, the absorption coefficient μa and the reduced scattering coefficient of onion tissues were calculated using the inverse adding-doubling method. The results indicated that the dry and wet skins had significantly higher μa and than the flesh at 633 nm. For both skins and flesh, the μa varied between cultivars, while the differences of the between cultivars were less profound. All types of onion tissues were high-albedo materials at 633 nm. Using the calculated optical properties, Monte Carlo simulations were performed to model the light propagation in 25 different scenarios of multi-layer onion tissues for four cultivars, respectively. The results showed that the incident light at 633 nm would lose 99% of its energy within 6 layers in any of the simulated scenarios, and the light penetrated more layers in the sweet onions than in the other three cultivars. This work provided fundamental understanding of the optical properties of onion tissues and the light propagation in onion bulbs at 633 nm. The investigation of the onion optical properties will be extended to a broader spectrum in the future.
Takeda, F.; Krewer, G.; Li, C.; MacLean, D.; Olmstead, J. W.
Techniques for increasing machine-harvest efficiency in southern highbush and rabbiteye blueberries Journal Article
In: Hort Technology, 23(4), 430-436, 2013.
@article{Takeda2013,
title = {Techniques for increasing machine-harvest efficiency in southern highbush and rabbiteye blueberries},
author = {F. Takeda and G. Krewer and C. Li and D. MacLean and J. W. Olmstead},
year = {2013},
date = {2013-07-17},
journal = {Hort Technology, 23(4), 430-436},
abstract = {Northern highbush (NH) blueberry (Vaccinium corymbosum) and southern highbush (SH) blueberry (V. corymbosum hybrids) have fruit that vary in firmness. The SH fruit is mostly hand harvested for the fresh market. Hand harvesting is labor-intensive requiring more than 500 hours/acre. Rabbiteye blueberry (V. virgatum) tends to have firmer fruit skin than that of NH blueberry and has been mostly machine harvested for the processing industry. Sparkleberry (V. arboreum) has very firm fruit. With the challenges of labor availability, efforts are under way to produce more marketable fruit using machine harvesting. This could require changing the design of harvesting machine and plant architecture, and the development of cultivars with fruit that will bruise less after impact with hard surfaces of machines. The objectives of this study were to determine the fruit quality of machine-harvested SH blueberry, analyze the effect of drop height and padding the contact surface on fruit quality, investigate the effect of crown restriction on ground loss, and determine the effect of plant size on machine harvestability. The fruit of ‘Farthing’, ‘Scintilla’, ‘Sweetcrisp’, and several selections were either hand harvested or machine harvested and assessed during postharvest storage for bruise damage and softening. Machine harvesting contributed to bruise damage in the fruit and softening in storage. The fruit of firm-textured SH blueberry (‘Farthing’, ‘Sweetcrisp’, and selection FL 05-528) was firmer than that of ‘Scintilla’ after 1 week in cold storage. Fruit drop tests from a height of 20 and 40 inches on a plastic surface showed that ‘Scintilla’ was more susceptible to bruising than that of firm-textured ‘Farthing’ and ‘Sweetcrisp’. When the contact surface was cushioned with a foam sheet, bruise incidence was significantly reduced in all SH blueberry used in the study. Also, the fruit dropped 40 inches developed more bruise damage than those dropped 20 inches. Ground loss during machine harvesting was reduced from 24% to 17% by modifying the rabbiteye blueberry plant architecture. Further modifications to harvesting machines and plant architecture are necessary to improve the quality of machine-harvested SH and rabbiteye blueberry fruit and the overall efficiency of blueberry (Vaccinium species and hybrids) harvesting machines.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Northern highbush (NH) blueberry (Vaccinium corymbosum) and southern highbush (SH) blueberry (V. corymbosum hybrids) have fruit that vary in firmness. The SH fruit is mostly hand harvested for the fresh market. Hand harvesting is labor-intensive requiring more than 500 hours/acre. Rabbiteye blueberry (V. virgatum) tends to have firmer fruit skin than that of NH blueberry and has been mostly machine harvested for the processing industry. Sparkleberry (V. arboreum) has very firm fruit. With the challenges of labor availability, efforts are under way to produce more marketable fruit using machine harvesting. This could require changing the design of harvesting machine and plant architecture, and the development of cultivars with fruit that will bruise less after impact with hard surfaces of machines. The objectives of this study were to determine the fruit quality of machine-harvested SH blueberry, analyze the effect of drop height and padding the contact surface on fruit quality, investigate the effect of crown restriction on ground loss, and determine the effect of plant size on machine harvestability. The fruit of ‘Farthing’, ‘Scintilla’, ‘Sweetcrisp’, and several selections were either hand harvested or machine harvested and assessed during postharvest storage for bruise damage and softening. Machine harvesting contributed to bruise damage in the fruit and softening in storage. The fruit of firm-textured SH blueberry (‘Farthing’, ‘Sweetcrisp’, and selection FL 05-528) was firmer than that of ‘Scintilla’ after 1 week in cold storage. Fruit drop tests from a height of 20 and 40 inches on a plastic surface showed that ‘Scintilla’ was more susceptible to bruising than that of firm-textured ‘Farthing’ and ‘Sweetcrisp’. When the contact surface was cushioned with a foam sheet, bruise incidence was significantly reduced in all SH blueberry used in the study. Also, the fruit dropped 40 inches developed more bruise damage than those dropped 20 inches. Ground loss during machine harvesting was reduced from 24% to 17% by modifying the rabbiteye blueberry plant architecture. Further modifications to harvesting machines and plant architecture are necessary to improve the quality of machine-harvested SH and rabbiteye blueberry fruit and the overall efficiency of blueberry (Vaccinium species and hybrids) harvesting machines.
Li, C.; Yu, P.; Takeda, F.; Krewer, G.
A miniature instrumented sphere to understand impacts created by mechanical blueberry harvesters Journal Article
In: HortTechnology, 23(4), 425-429, 2013.
@article{Li2013,
title = {A miniature instrumented sphere to understand impacts created by mechanical blueberry harvesters},
author = {C. Li and P. Yu and F. Takeda and G. Krewer},
year = {2013},
date = {2013-06-07},
journal = {HortTechnology, 23(4), 425-429},
abstract = {The majority of U.S. northern highbush blueberry (Vaccinium corymbosum) and southern highbush blueberry (V. corymbosum hybrids) for the fresh market is hand harvested because of the high bruising damage to the fruit caused by current machine harvesters. To reduce bruising, it is important to understand how the harvester’s machine parts interact with the fruit. A miniature instrumented sphere, hereafter referred to as Smart Berry, was developed to mimic a blueberry (Vaccinium species and hybrids) fruit and to quantitatively measure mechanical impacts experienced by a real blueberry fruit during mechanical harvesting. The Smart Berry sensor recorded impacts using three single-axis accelerometers with a maximum sampling frequency of 3 kHz and ±500 gn sensing range. Calibration tests showed that the maximum error of the measurement was 0.53% of the output span. The diameter of the sensor (1 inch) was only half of that for the current smallest instrumented sphere on the market. Used together with a close-up video, the fully calibrated sensors were used to identify and measure mechanical impacts occurring in a commercial rotary blueberry harvester. The data suggested that the catch pan created the largest single mechanical impacts. Thus, reducing the drop height or padding the surface could be effective measures to reduce bruising damage caused by the catch pans. The Smart Berry was also used to compare harvesters with two different detaching mechanisms. The rotary detaching mechanism created significantly fewer and lower-magnitude impacts than the slapper mechanism (P ≤ 0.05). Manual drop tests demonstrated that the impact data recorded by the Smart Berry can be correlated with bruising damage experienced by blueberry fruit. Taken together, the data can be used to improve the design of the current machine harvesters for reduction of bruising damage to blueberry fruit destined for the fresh market, and potentially lead to enhanced highbush blueberry production efficiency in the long run.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The majority of U.S. northern highbush blueberry (Vaccinium corymbosum) and southern highbush blueberry (V. corymbosum hybrids) for the fresh market is hand harvested because of the high bruising damage to the fruit caused by current machine harvesters. To reduce bruising, it is important to understand how the harvester’s machine parts interact with the fruit. A miniature instrumented sphere, hereafter referred to as Smart Berry, was developed to mimic a blueberry (Vaccinium species and hybrids) fruit and to quantitatively measure mechanical impacts experienced by a real blueberry fruit during mechanical harvesting. The Smart Berry sensor recorded impacts using three single-axis accelerometers with a maximum sampling frequency of 3 kHz and ±500 gn sensing range. Calibration tests showed that the maximum error of the measurement was 0.53% of the output span. The diameter of the sensor (1 inch) was only half of that for the current smallest instrumented sphere on the market. Used together with a close-up video, the fully calibrated sensors were used to identify and measure mechanical impacts occurring in a commercial rotary blueberry harvester. The data suggested that the catch pan created the largest single mechanical impacts. Thus, reducing the drop height or padding the surface could be effective measures to reduce bruising damage caused by the catch pans. The Smart Berry was also used to compare harvesters with two different detaching mechanisms. The rotary detaching mechanism created significantly fewer and lower-magnitude impacts than the slapper mechanism (P ≤ 0.05). Manual drop tests demonstrated that the impact data recorded by the Smart Berry can be correlated with bruising damage experienced by blueberry fruit. Taken together, the data can be used to improve the design of the current machine harvesters for reduction of bruising damage to blueberry fruit destined for the fresh market, and potentially lead to enhanced highbush blueberry production efficiency in the long run.
2012
Li, C.; Thibodeaux, D.; Knowlton, A.; Foulk, J.
Effect of cleaning treatments and cotton variety on fiber and yarn quality Journal Article
In: Applied Engineering in Agriculture, 28(6), 833-840, 2012.
BibTeX | Tags:
@article{Li2012,
title = {Effect of cleaning treatments and cotton variety on fiber and yarn quality},
author = {C. Li and D. Thibodeaux and A. Knowlton and J. Foulk},
year = {2012},
date = {2012-09-13},
journal = {Applied Engineering in Agriculture, 28(6), 833-840},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wang, H.; Li, C.; Mei, L.; Li, M.
Integration and Calibration of A Line-Scan Hyperspectral Imaging System Journal Article
In: Transactions of the Chinese Society of Agricultural Engineering, 2012.
BibTeX | Tags:
@article{Wang2012b,
title = {Integration and Calibration of A Line-Scan Hyperspectral Imaging System},
author = {H. Wang and C. Li and L. Mei and M. Li},
year = {2012},
date = {2012-09-04},
journal = {Transactions of the Chinese Society of Agricultural Engineering},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Yu, P.; Li, C.; Takeda, F.; Krewer, G.; Rains, G.; Hamrita, T.
Quantitative evaluation of a blueberry mechanical harvester using a miniature instrumented sphere Journal Article
In: Computers and Electronics in Agriculture, 88, 25-31, 2012.
Abstract | Links | BibTeX | Tags:
@article{Yu2012,
title = {Quantitative evaluation of a blueberry mechanical harvester using a miniature instrumented sphere},
author = {P. Yu and C. Li and F. Takeda and G. Krewer and G. Rains and T. Hamrita},
doi = {10.1016/j.compag.2012.06.005},
year = {2012},
date = {2012-06-22},
journal = {Computers and Electronics in Agriculture, 88, 25-31},
abstract = {Southern highbush blueberries (Vaccinium corymbosum interspecific hybrids) are predominantly for the fresh market and, with a few exceptions, are hand harvested. Though mechanical harvesting systems are available for processed blueberries, low harvest efficiency and high fruit damage have limited their use for picking blueberries for fresh market. To improve the current machine harvester design, it is apparent that the interaction between the machine harvester and fruit should be well understood. The goal of this study was, therefore, to provide such an understanding by using a custom-made miniature instrumented sphere. The miniature Berry Impact Recording Device (BIRD) was used to measure the mechanical impacts created by a rotary mechanical harvester during fruit harvesting. A closeup video recorded the harvesting to pinpoint critical control points where most impacts were created. The results showed that the catch plates on the rotary harvester accounted for over 30% of all mechanical impacts imposed on the BIRD, followed by the empty fruit collection box (lug) (>20%). Impacts created by the conveyer belt and shaking rods combined accounted for only 25% of mechanical impacts. Thus, the most significant reduction in bruising could be achieved through modifications in the catch plates and lugs. The impact of three contacting surfaces (the catch plates, conveyer belt, and steel tunnel) on the harvester and three commercial padding materials (Cellular Silicone, Slow Rebound, and No Bruze) were evaluated. Harvester surface evaluations revealed that the catch plate was the hardest surface. Among the three padding materials evaluated, the Cellular Silicone provided the best cushioning. This study proved the efficacy of using a customized miniature instrumented sphere in measuring mechanical impacts created by various machine parts during harvesting. It provided a better understanding of how the berries interact with different machine parts of a rotary harvester.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Southern highbush blueberries (Vaccinium corymbosum interspecific hybrids) are predominantly for the fresh market and, with a few exceptions, are hand harvested. Though mechanical harvesting systems are available for processed blueberries, low harvest efficiency and high fruit damage have limited their use for picking blueberries for fresh market. To improve the current machine harvester design, it is apparent that the interaction between the machine harvester and fruit should be well understood. The goal of this study was, therefore, to provide such an understanding by using a custom-made miniature instrumented sphere. The miniature Berry Impact Recording Device (BIRD) was used to measure the mechanical impacts created by a rotary mechanical harvester during fruit harvesting. A closeup video recorded the harvesting to pinpoint critical control points where most impacts were created. The results showed that the catch plates on the rotary harvester accounted for over 30% of all mechanical impacts imposed on the BIRD, followed by the empty fruit collection box (lug) (>20%). Impacts created by the conveyer belt and shaking rods combined accounted for only 25% of mechanical impacts. Thus, the most significant reduction in bruising could be achieved through modifications in the catch plates and lugs. The impact of three contacting surfaces (the catch plates, conveyer belt, and steel tunnel) on the harvester and three commercial padding materials (Cellular Silicone, Slow Rebound, and No Bruze) were evaluated. Harvester surface evaluations revealed that the catch plate was the hardest surface. Among the three padding materials evaluated, the Cellular Silicone provided the best cushioning. This study proved the efficacy of using a customized miniature instrumented sphere in measuring mechanical impacts created by various machine parts during harvesting. It provided a better understanding of how the berries interact with different machine parts of a rotary harvester.
Wang, W.; Li, C.; Tollner, E. W.; Rains, G. C.
Development of software for spectral imaging data acquisition using LabVIEW Journal Article
In: Computers and Electronics in Agriculture, 84, 68-75, 2012.
Abstract | Links | BibTeX | Tags:
@article{Wang2012,
title = {Development of software for spectral imaging data acquisition using LabVIEW},
author = {W. Wang and C. Li and E.W. Tollner and G.C. Rains},
doi = {10.1016/j.compag.2012.02.010},
year = {2012},
date = {2012-02-20},
journal = {Computers and Electronics in Agriculture, 84, 68-75},
abstract = {Developing data acquisition software is a major challenge in integrating a spectral imaging system. This paper presents the design and implementation of a data acquisition program using LabVIEW for a liquid crystal tunable filter based spectral imaging system (900–1700 nm). The module-based program was designed in a three-tier structure. The image acquisition process, modelled by a finite state machine, was implemented in LabVIEW to control the spectral imaging system to collect hyperspectral or multispectral images. The collected spectral images were encoded in general format and could be further processed by other common spectral image analysis tools. In addition, the program could be used to observe band ratio images of the test object in real-time, collect spectral images after ensemble averaging, and select region of interest for spectral image acquisitions. This program is a useful data acquisition tool for the filter-based spectral imaging system. The design and implementation techniques described in this article could also be used to develop similar spectral image acquisition programs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Developing data acquisition software is a major challenge in integrating a spectral imaging system. This paper presents the design and implementation of a data acquisition program using LabVIEW for a liquid crystal tunable filter based spectral imaging system (900–1700 nm). The module-based program was designed in a three-tier structure. The image acquisition process, modelled by a finite state machine, was implemented in LabVIEW to control the spectral imaging system to collect hyperspectral or multispectral images. The collected spectral images were encoded in general format and could be further processed by other common spectral image analysis tools. In addition, the program could be used to observe band ratio images of the test object in real-time, collect spectral images after ensemble averaging, and select region of interest for spectral image acquisitions. This program is a useful data acquisition tool for the filter-based spectral imaging system. The design and implementation techniques described in this article could also be used to develop similar spectral image acquisition programs.
2011
Wang, W.; Li, C.; Tollner, E. W.; Gitaitis, R. D.; Rains, G. C.
Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderia cepacia)-infected onions Journal Article
In: Journal of Food Engineering, 109(1), 38-48, 2011.
Abstract | Links | BibTeX | Tags:
@article{Wang2011,
title = {Shortwave infrared hyperspectral imaging for detecting sour skin (Burkholderia cepacia)-infected onions},
author = {W. Wang and C. Li and E.W. Tollner and R.D. Gitaitis and G.C. Rains},
doi = {10.1016/j.jfoodeng.2011.10.001},
year = {2011},
date = {2011-10-08},
journal = {Journal of Food Engineering, 109(1), 38-48},
abstract = {Sour skin (Burkholderiacepacia) is a major postharvest disease for onions and causes substantial production and economic losses in onion postharvest. In this study, a shortwave infrared hyperspectral imaging system was explored to detect sour skin. The hyperspectral reflectance images (950–1650 nm) of onions were obtained for the healthy and sour skin-infected onions. Principal component analysis conducted on the spectra of the healthy and sour skin-infected onions suggested that the neck area of the onion at two wavelengths (1070 and 1400 nm) was most indicative of the sour skin. Log-ratio images utilizing the two optimal wavelengths were used for two different image analysis approaches. The first method applied a global threshold (0.45) to segregate the sour skin-infected areas from log-ratio images. Using the pixel number of the segregated areas, Fisher’s discriminant analysis recognized 80% healthy and sour skin-infected onions. The second classification approach used three parameters (max, contrast, and homogeneity) of the log-ratio images as the input features of support vector machine (Gaussian kernel, γ = 1.5), which discriminated 87.14% healthy and sour skin-infected onions. The result of this study can be used to further develop a multispectral imaging system to detect sour skin-infected onions on packing lines.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sour skin (Burkholderiacepacia) is a major postharvest disease for onions and causes substantial production and economic losses in onion postharvest. In this study, a shortwave infrared hyperspectral imaging system was explored to detect sour skin. The hyperspectral reflectance images (950–1650 nm) of onions were obtained for the healthy and sour skin-infected onions. Principal component analysis conducted on the spectra of the healthy and sour skin-infected onions suggested that the neck area of the onion at two wavelengths (1070 and 1400 nm) was most indicative of the sour skin. Log-ratio images utilizing the two optimal wavelengths were used for two different image analysis approaches. The first method applied a global threshold (0.45) to segregate the sour skin-infected areas from log-ratio images. Using the pixel number of the segregated areas, Fisher’s discriminant analysis recognized 80% healthy and sour skin-infected onions. The second classification approach used three parameters (max, contrast, and homogeneity) of the log-ratio images as the input features of support vector machine (Gaussian kernel, γ = 1.5), which discriminated 87.14% healthy and sour skin-infected onions. The result of this study can be used to further develop a multispectral imaging system to detect sour skin-infected onions on packing lines.
Wang, W.; Li, C.; Tollner, E. W.; Rains, G. C.; Gitaitis, R. D.
A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization Journal Article
In: Computers and Electronics in Agriculture, 80, 135-144, 2011.
Abstract | Links | BibTeX | Tags:
@article{Wang2011c,
title = {A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization},
author = {W. Wang and C. Li and E.W. Tollner and G.C. Rains and R.D. Gitaitis},
doi = {10.1016/j.compag.2011.09.003},
year = {2011},
date = {2011-09-09},
journal = {Computers and Electronics in Agriculture, 80, 135-144},
abstract = {Calibration is a critical step for developing spectral imaging systems. This paper presents a systematic calibration and characterization approach for a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. A series of tests were conducted to validate the linearity of the system output, measure the field of view of the spectral imager, increase the system spectral sensitivity, test the spatial and spectral resolution of the system, evaluate the system stability and image distortion, and reduce the spectral noise of the system output. Results showed that the system had an angle of view of 6.98° and a spatial resolution of 158 μm. The spectral sensitivity of the system was corrected by controlling the camera exposure time and gain, which increased the signal to noise ratio of the system by 16.5%. Test results also verified the system spectral accuracy and linearity (r > 0.999). The system output was proven to be stable and image distortion was not perceivable. Results of calibration tests indicated that this system satisfied the design criteria in both spatial and spectral domains. The calibration methods presented here are applicable to the LCTF-based spectral imaging systems in other applications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Calibration is a critical step for developing spectral imaging systems. This paper presents a systematic calibration and characterization approach for a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. A series of tests were conducted to validate the linearity of the system output, measure the field of view of the spectral imager, increase the system spectral sensitivity, test the spatial and spectral resolution of the system, evaluate the system stability and image distortion, and reduce the spectral noise of the system output. Results showed that the system had an angle of view of 6.98° and a spatial resolution of 158 μm. The spectral sensitivity of the system was corrected by controlling the camera exposure time and gain, which increased the signal to noise ratio of the system by 16.5%. Test results also verified the system spectral accuracy and linearity (r > 0.999). The system output was proven to be stable and image distortion was not perceivable. Results of calibration tests indicated that this system satisfied the design criteria in both spatial and spectral domains. The calibration methods presented here are applicable to the LCTF-based spectral imaging systems in other applications.
Yu, P.; Li, C.; Rains, G.; Hamrita, T.
Development of the Blueberry Impact Recording Device sensing system: hardware design and calibration Journal Article
In: Computers and Electronics in Agriculture, 79(2), 103-111, 2011.
Abstract | Links | BibTeX | Tags:
@article{Yu2011,
title = {Development of the Blueberry Impact Recording Device sensing system: hardware design and calibration},
author = {P. Yu and C. Li and G. Rains and T. Hamrita},
doi = {10.1016/j.compag.2011.08.013},
year = {2011},
date = {2011-08-29},
journal = {Computers and Electronics in Agriculture, 79(2), 103-111},
abstract = {Bruising caused by the impact damage occurs frequently during mechanical harvest process for highbush blueberries. The overall goal of this study was to develop a miniature and low-cost sensor prototype to quantitatively measure the impact forces endured by blueberries during the mechanical harvest process, which could be used to reduce blueberry bruising through improved harvester design. The sensing system developed in this study had three essential components: the sensor, the interface box, and the computer software program. The round circuit board of sensor is less than one inch (19.4 mm), including three accelerometers with ±500 g sensing range in each orthogonal axis, one eight-bit microcontroller, one 128 KB memory chip, and other electronic components with low power consumption. The sensor board and rechargeable battery were cast into a one inch (25.4 mm) sphere using silicone rubber. The interface box serves as the intermediate communication platform to connect the sensor and the computer. The PC-software retrieves data from the sensor via the I2C communication and downloads data to a computer for further analysis via the RS232 communication. The sensor was calibrated using a centrifuge. The accuracy of the sensor output was 0.53% (2.60 g maximum deviation) and −0.33% (−1.26 g maximum deviation), with precision error of 0.63% (3.21 g) in the output span. This miniature and low-cost sensor prototype provides the opportunity to understand how the berry (or other small fruits) interacts with different machine parts within the harvester and to identify critical control points that cause the most mechanical impacts, which was not achievable in the past.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bruising caused by the impact damage occurs frequently during mechanical harvest process for highbush blueberries. The overall goal of this study was to develop a miniature and low-cost sensor prototype to quantitatively measure the impact forces endured by blueberries during the mechanical harvest process, which could be used to reduce blueberry bruising through improved harvester design. The sensing system developed in this study had three essential components: the sensor, the interface box, and the computer software program. The round circuit board of sensor is less than one inch (19.4 mm), including three accelerometers with ±500 g sensing range in each orthogonal axis, one eight-bit microcontroller, one 128 KB memory chip, and other electronic components with low power consumption. The sensor board and rechargeable battery were cast into a one inch (25.4 mm) sphere using silicone rubber. The interface box serves as the intermediate communication platform to connect the sensor and the computer. The PC-software retrieves data from the sensor via the I2C communication and downloads data to a computer for further analysis via the RS232 communication. The sensor was calibrated using a centrifuge. The accuracy of the sensor output was 0.53% (2.60 g maximum deviation) and −0.33% (−1.26 g maximum deviation), with precision error of 0.63% (3.21 g) in the output span. This miniature and low-cost sensor prototype provides the opportunity to understand how the berry (or other small fruits) interacts with different machine parts within the harvester and to identify critical control points that cause the most mechanical impacts, which was not achievable in the past.
Wang, W.; Li, C.; Tollner, E. W.; Rains, G. C.; Gitaitis, R. D.
A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration Journal Article
In: Computers and Electronics in Agriculture, 80, 126-134, 2011.
Abstract | Links | BibTeX | Tags:
@article{Wang2011b,
title = {A liquid crystal tunable filter based shortwave infrared spectral imaging system: Design and integration},
author = {W. Wang and C. Li and E.W. Tollner and G.C. Rains and R.D. Gitaitis},
doi = {10.1016/j.compag.2011.07.012},
year = {2011},
date = {2011-07-28},
journal = {Computers and Electronics in Agriculture, 80, 126-134},
abstract = {This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents the methodology to design and integrate a liquid crystal tunable filter (LCTF) based shortwave infrared (SWIR) spectral imaging system. The system consisted of an LCTF-based SWIR spectral imager, an illumination unit, a frame grabber, and a computer with the data acquisition software. The spectral imager included an InGaAs camera (320 × 256 pixels), an SWIR lens (50 mm, F/1.4), and an LCTF (20 mm aperture). Four multifaceted reflector halogen lamps (35 W, 12 VDC) were used to build the illumination unit. The system was integrated by a LabVIEW program for data acquisition. It can capture hyperspectral or multispectral images of the test object in the spectral range of 900–1700 nm. The system was validated by differentiating sugar from wheat flour, and water from 95% ethanol. The results showed that the system can distinguish these materials in both spectral and spatial domains. This SWIR spectral imaging system could be a potential useful tool for nondestructive inspection of food quality and safety.
Lin, T.; Rodriguez, L. F.; Li, C.; Eckhoff, S. R.
An engineering and economic evaluation of wet and dry pre-fractionation processes for dry-grind ethanol facilities Journal Article
In: Bioresource Technology, 102(19), 9013-9019, 2011.
Abstract | Links | BibTeX | Tags:
@article{Lin2011,
title = {An engineering and economic evaluation of wet and dry pre-fractionation processes for dry-grind ethanol facilities},
author = {T. Lin and L.F. Rodriguez and C. Li and S.R. Eckhoff},
doi = {10.1016/j.biortech.2011.06.013},
year = {2011},
date = {2011-06-16},
journal = {Bioresource Technology, 102(19), 9013-9019},
abstract = {An engineering–economic model was developed to compare the profitability of the wet fractionation process, a generic dry fractionation process, and the conventional dry grind process. Under market conditions as of January 2011, only fractionation processes generated a positive cash flow. Reduced unit manufacturing costs and increased ethanol production capacity were two major contributions. Corn and ethanol price sensitivity analysis showed that the wet fractionation process always outperformed a generic dry fractionation process at any scenario considered in this research. A generic dry fractionation process would provide better economic performance than the conventional dry grind process if corn price was low and ethanol price was high. All three processes would perform more resiliently if the DDGS price was determined by its composition.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An engineering–economic model was developed to compare the profitability of the wet fractionation process, a generic dry fractionation process, and the conventional dry grind process. Under market conditions as of January 2011, only fractionation processes generated a positive cash flow. Reduced unit manufacturing costs and increased ethanol production capacity were two major contributions. Corn and ethanol price sensitivity analysis showed that the wet fractionation process always outperformed a generic dry fractionation process at any scenario considered in this research. A generic dry fractionation process would provide better economic performance than the conventional dry grind process if corn price was low and ethanol price was high. All three processes would perform more resiliently if the DDGS price was determined by its composition.
Yu, P.; Li, C.; Rains, G.; Hamrita, T.
Development of the Berry Impact Recording Device Sensing System: Software Journal Article
In: Computers and Electronics in Agriculture, 77(2), 195-203, 2011.
Abstract | Links | BibTeX | Tags:
@article{Yu2011b,
title = {Development of the Berry Impact Recording Device Sensing System: Software},
author = {P. Yu and C. Li and G. Rains and T. Hamrita},
doi = {10.1016/j.compag.2011.05.003},
year = {2011},
date = {2011-05-07},
journal = {Computers and Electronics in Agriculture, 77(2), 195-203},
abstract = {This paper reports a complete impact data acquisition, processing, and analyzing software system that applies on the hardware platform of the Berry Impact Recording Device (BIRD). The software has three major sections that correspond to the hardware: The BIRD sensor program, the interface box program, and the computer software i-BIRD. The sensor program samples acceleration data from three axes and records them as single impacts with a maximum sampling rate of 3.0 kHz. Users can configure the sensor via the i-BIRD computer software, with different options of sampling frequencies (682–3050 Hz) and thresholds (0–205 g, where g is the gravitational acceleration). The data recorded can be downloaded, processed and graphically displayed on the computer. A real time clock was created using the interrupt service routine provided by the microcontroller. The accuracy of the sensor’s clock was calibrated with an error of 0.073%, which was adequate to record impact data in this application. The shape of impact curves recorded by the BIRD sensor at three sampling frequencies (682, 998, and 1480 Hz) matched well with the curves recorded by a high frequency (10 kHz) data logger with the maximum root mean squared error of 4.4 g. The velocity change had a relative error less than 5%. With confirmation of all those performances, the software system enabled the BIRD to be a useful tool to collect impact data during small fruit (such as blueberry) mechanical harvest.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper reports a complete impact data acquisition, processing, and analyzing software system that applies on the hardware platform of the Berry Impact Recording Device (BIRD). The software has three major sections that correspond to the hardware: The BIRD sensor program, the interface box program, and the computer software i-BIRD. The sensor program samples acceleration data from three axes and records them as single impacts with a maximum sampling rate of 3.0 kHz. Users can configure the sensor via the i-BIRD computer software, with different options of sampling frequencies (682–3050 Hz) and thresholds (0–205 g, where g is the gravitational acceleration). The data recorded can be downloaded, processed and graphically displayed on the computer. A real time clock was created using the interrupt service routine provided by the microcontroller. The accuracy of the sensor’s clock was calibrated with an error of 0.073%, which was adequate to record impact data in this application. The shape of impact curves recorded by the BIRD sensor at three sampling frequencies (682, 998, and 1480 Hz) matched well with the curves recorded by a high frequency (10 kHz) data logger with the maximum root mean squared error of 4.4 g. The velocity change had a relative error less than 5%. With confirmation of all those performances, the software system enabled the BIRD to be a useful tool to collect impact data during small fruit (such as blueberry) mechanical harvest.
Li, C.; Luo, J.; MacLean, D.
A novel instrument to delineate varietal and harvest effect on blueberry fruit texture during storage Journal Article
In: Journal of the Science of Food and Agriculture, 91(9), 1653-1658, 2011.
Abstract | Links | BibTeX | Tags:
@article{Li2011,
title = {A novel instrument to delineate varietal and harvest effect on blueberry fruit texture during storage},
author = {C. Li and J. Luo and D. MacLean},
doi = {10.1002/jsfa.4362},
year = {2011},
date = {2011-03-28},
journal = {Journal of the Science of Food and Agriculture, 91(9), 1653-1658},
abstract = {Firmness is an important quality index for blueberries. It is the major factor that determines consumer acceptability, storability and resistance to injury and diseases during storage and fresh marketing. Blueberry cultivars vary in their firmness, with southern highbush cultivars usually softer than Rabbiteye blueberries. In this study, varietal and harvest effects on blueberry firmness were measured by the Firmtech II and laser air-puff instruments. This was the first time that the laser air-puff, a non-contact food firmness tester, had been used for firmness testing of small fruit, such as blueberry.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Firmness is an important quality index for blueberries. It is the major factor that determines consumer acceptability, storability and resistance to injury and diseases during storage and fresh marketing. Blueberry cultivars vary in their firmness, with southern highbush cultivars usually softer than Rabbiteye blueberries. In this study, varietal and harvest effects on blueberry firmness were measured by the Firmtech II and laser air-puff instruments. This was the first time that the laser air-puff, a non-contact food firmness tester, had been used for firmness testing of small fruit, such as blueberry.
Li, C.; Knowlton, A.; Brown, S.; Ritchie, G.
A Comparative Study of a Microgin with a Lab Gin Stand and Commercial Gins in Southeast United States Journal Article
In: Applied Engineering in Agriculture, 27(2), 167-175, 2011.
Abstract | Links | BibTeX | Tags:
@article{Li2011b,
title = {A Comparative Study of a Microgin with a Lab Gin Stand and Commercial Gins in Southeast United States},
author = {C. Li and A. Knowlton and S. Brown and G. Ritchie},
doi = {10.13031/2013.36488},
year = {2011},
date = {2011-03-10},
journal = {Applied Engineering in Agriculture, 27(2), 167-175},
abstract = {A microgin is built to simulate the performance of commercial gins and to gin cotton samples from whole research plots. This study compared a modern microgin located in the southeast United States, a lab gin stand, and five commercial gins in terms of fiber quality properties and lint turnout. Results showed that the lint turnout from the laboratory gin was consistently higher (0.8-2.3%) than that from the commercial gins, while the microgin and commercial gins had similar gin turnout (0.1-1.6% differences). The HVI trash and leaf grade from the lab gin stand were 0.98 (units) and 3 (grades) higher than that from the commercial gins, respectively; the differences between the microgin and commercial gins were only 0.16 and 1, respectively. Color reflectance from the lab gin was 5.92% less than that from the commercial gins; the difference between the microgin and commercial gins in reflectance was only 0.68%. Length and uniformity from the lab gin were 1.02 mm and 1.7% higher than those from the commercial gins, respectively; the differences between the microgin and commercial gins were only 0.25 mm and 0.58%, respectively. Linear regression analyses showed that the microgin had a consistently lower bias than the lab gin stand in estimating commercial gin values in most fiber quality properties except for micronaire. The data confirmed that the microgin outperforms the lab gin stand in estimating the lint turnout and most fiber quality properties and it should be a valuable tool for cotton research. © 2011 American Society of Agricultural and Biological Engineers.
A Comparative Study of a Microgin with a Lab Gin Stand and Commercial Gins in Southeast United States. Available from: https://www.researchgate.net/publication/275590005_A_Comparative_Study_of_a_Microgin_with_a_Lab_Gin_Stand_and_Commercial_Gins_in_Southeast_United_States [accessed Sep 24 2018].},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A microgin is built to simulate the performance of commercial gins and to gin cotton samples from whole research plots. This study compared a modern microgin located in the southeast United States, a lab gin stand, and five commercial gins in terms of fiber quality properties and lint turnout. Results showed that the lint turnout from the laboratory gin was consistently higher (0.8-2.3%) than that from the commercial gins, while the microgin and commercial gins had similar gin turnout (0.1-1.6% differences). The HVI trash and leaf grade from the lab gin stand were 0.98 (units) and 3 (grades) higher than that from the commercial gins, respectively; the differences between the microgin and commercial gins were only 0.16 and 1, respectively. Color reflectance from the lab gin was 5.92% less than that from the commercial gins; the difference between the microgin and commercial gins in reflectance was only 0.68%. Length and uniformity from the lab gin were 1.02 mm and 1.7% higher than those from the commercial gins, respectively; the differences between the microgin and commercial gins were only 0.25 mm and 0.58%, respectively. Linear regression analyses showed that the microgin had a consistently lower bias than the lab gin stand in estimating commercial gin values in most fiber quality properties except for micronaire. The data confirmed that the microgin outperforms the lab gin stand in estimating the lint turnout and most fiber quality properties and it should be a valuable tool for cotton research. © 2011 American Society of Agricultural and Biological Engineers.
A Comparative Study of a Microgin with a Lab Gin Stand and Commercial Gins in Southeast United States. Available from: https://www.researchgate.net/publication/275590005_A_Comparative_Study_of_a_Microgin_with_a_Lab_Gin_Stand_and_Commercial_Gins_in_Southeast_United_States [accessed Sep 24 2018].
A Comparative Study of a Microgin with a Lab Gin Stand and Commercial Gins in Southeast United States. Available from: https://www.researchgate.net/publication/275590005_A_Comparative_Study_of_a_Microgin_with_a_Lab_Gin_Stand_and_Commercial_Gins_in_Southeast_United_States [accessed Sep 24 2018].
2010
Li, C.; Gitaitis, R. D.; Schmidt, N.
Detection of onion postharvest diseases by analyses of headspace volatiles using a gas sensor array and GC-MS Journal Article
In: LWT – Food Science and Technology, 44(4), 1019-1025, 2010.
Abstract | Links | BibTeX | Tags:
@article{Li2010b,
title = {Detection of onion postharvest diseases by analyses of headspace volatiles using a gas sensor array and GC-MS},
author = {C. Li and R.D. Gitaitis and N. Schmidt},
doi = {10.1016/j.lwt.2010.11.036},
year = {2010},
date = {2010-11-23},
journal = {LWT – Food Science and Technology, 44(4), 1019-1025},
abstract = {Onion postharvest diseases cause significant losses in storage. Volatile sensing by the gas sensor array technology could be used as a promising alternative method to detect onion diseases. Onions were inoculated with Botrytis allii and Burkholderia cepacia, causal pathogen for Botrytis neck rot and sour skin, respectively. In the first phase of this study, 30 onions with equal number of B. allii inoculated and control healthy onions were measured by the gas sensor array from 8 to 11 days after inoculation (dai) and the principal component analysis (PCA) score plot demonstrated that the gas sensor array responded differently to Botrytis neck rot infected onions from those of healthy onions. In the second phase, 30 onions with 10 for each of the three treatments (Botrytis neck rot, sour skin, control) were measured by the gas sensor array on 5, 6, and 7 dai. The PCA score plot illustrated that three treatments formed three distinct clusters, while a hierarchical cluster analysis dendrogram indicated that the response of the gas sensor array to Botrytis neck rot and sour skin were similar. The correct classification rate of the linear discriminant model for three treatments was over 97.8%. Results from GC-MS showed that total 24 major volatiles were identified from the headspace of three treatments. Sixteen compounds were uniquely present in B. allii and B. cepacia inoculated onion bulbs. Total amount of volatile compounds detected in pathogen inoculated bulbs was one to two orders of magnitude higher than that of control healthy bulbs. This study demonstrated the feasibility of using a gas sensor array to detect two onion postharvest diseases in storage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Onion postharvest diseases cause significant losses in storage. Volatile sensing by the gas sensor array technology could be used as a promising alternative method to detect onion diseases. Onions were inoculated with Botrytis allii and Burkholderia cepacia, causal pathogen for Botrytis neck rot and sour skin, respectively. In the first phase of this study, 30 onions with equal number of B. allii inoculated and control healthy onions were measured by the gas sensor array from 8 to 11 days after inoculation (dai) and the principal component analysis (PCA) score plot demonstrated that the gas sensor array responded differently to Botrytis neck rot infected onions from those of healthy onions. In the second phase, 30 onions with 10 for each of the three treatments (Botrytis neck rot, sour skin, control) were measured by the gas sensor array on 5, 6, and 7 dai. The PCA score plot illustrated that three treatments formed three distinct clusters, while a hierarchical cluster analysis dendrogram indicated that the response of the gas sensor array to Botrytis neck rot and sour skin were similar. The correct classification rate of the linear discriminant model for three treatments was over 97.8%. Results from GC-MS showed that total 24 major volatiles were identified from the headspace of three treatments. Sixteen compounds were uniquely present in B. allii and B. cepacia inoculated onion bulbs. Total amount of volatile compounds detected in pathogen inoculated bulbs was one to two orders of magnitude higher than that of control healthy bulbs. This study demonstrated the feasibility of using a gas sensor array to detect two onion postharvest diseases in storage.
Adedoyin, A.; Li, C.; Toews, M.
Characterization of single cotton fibers using a laser diffraction system Journal Article
In: Textile Research Journal, 81(4), 355-367, 2010.
Abstract | Links | BibTeX | Tags:
@article{Adedoyin2010,
title = {Characterization of single cotton fibers using a laser diffraction system},
author = {A. Adedoyin and C. Li and M. Toews},
doi = {10.1177/0040517510387210},
year = {2010},
date = {2010-11-22},
journal = {Textile Research Journal, 81(4), 355-367},
abstract = {The fineness and maturity of a cotton fiber is determined by its cross-sectional perimeter and area. However, measuring fiber cross-sectional perimeter and area is an extremely tedious and challenging task. This paper presents an alternative approach to measure fiber longitudinal width using Fraunhofer diffraction patterns, which can be used to estimate fiber fineness and maturity. We designed a laser diffraction system, developed a software program to denoise and process diffraction patterns, and tested the system on a uniform iron wire and individual cotton fibers from ten bales. The width of the iron wire measured by our system was 25.43 μm (0.1% different from the nominal value) with a 0.01 μm standard error of the mean. Cotton fiber width measured by the laser diffraction system could differentiate among ten cotton bales, which was in accordance with their relative differences in fineness. Linear regression analyses revealed that strong linear relationships exist between the fiber width and cross-sectional perimeter and area (r2 = 0.81 and 0.77, respectively), as well as between the fiber width and fineness and micronaire (r2 = 0.79 and 0.73, respectively) (n = 10, p = 0.01). This simple, replicable, and relatively inexpensive optical system could be used for single fiber longitudinal profile characterization and cotton fineness and maturity estimation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The fineness and maturity of a cotton fiber is determined by its cross-sectional perimeter and area. However, measuring fiber cross-sectional perimeter and area is an extremely tedious and challenging task. This paper presents an alternative approach to measure fiber longitudinal width using Fraunhofer diffraction patterns, which can be used to estimate fiber fineness and maturity. We designed a laser diffraction system, developed a software program to denoise and process diffraction patterns, and tested the system on a uniform iron wire and individual cotton fibers from ten bales. The width of the iron wire measured by our system was 25.43 μm (0.1% different from the nominal value) with a 0.01 μm standard error of the mean. Cotton fiber width measured by the laser diffraction system could differentiate among ten cotton bales, which was in accordance with their relative differences in fineness. Linear regression analyses revealed that strong linear relationships exist between the fiber width and cross-sectional perimeter and area (r2 = 0.81 and 0.77, respectively), as well as between the fiber width and fineness and micronaire (r2 = 0.79 and 0.73, respectively) (n = 10, p = 0.01). This simple, replicable, and relatively inexpensive optical system could be used for single fiber longitudinal profile characterization and cotton fineness and maturity estimation.
Lee, W. S.; Alchanatis, V.; Yang, C.; Hirafuji, M.; Moshou, D.; Li, C.
Sensing technologies for precision specialty crop production Journal Article
In: Computers and Electronics in Agriculture, 74(1), 2-33, 2010.
Abstract | Links | BibTeX | Tags:
@article{Lee2010,
title = {Sensing technologies for precision specialty crop production},
author = {W. S. Lee and V. Alchanatis and C. Yang and M. Hirafuji and D. Moshou and C. Li},
doi = {10.1016/j.compag.2010.08.005},
year = {2010},
date = {2010-07-30},
journal = {Computers and Electronics in Agriculture, 74(1), 2-33},
abstract = {With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the advances in electronic and information technologies, various sensing systems have been developed for specialty crop production around the world. Accurate information concerning the spatial variability within fields is very important for precision farming of specialty crops. However, this variability is affected by a variety of factors, including crop yield, soil properties and nutrients, crop nutrients, crop canopy volume and biomass, water content, and pest conditions (disease, weeds, and insects). These factors can be measured using diverse types of sensors and instruments such as field-based electronic sensors, spectroradiometers, machine vision, airborne multispectral and hyperspectral remote sensing, satellite imagery, thermal imaging, RFID, and machine olfaction system, among others. Sensing techniques for crop biomass detection, weed detection, soil properties and nutrients are most advanced and can provide the data required for site specific management. On the other hand, sensing techniques for diseases detection and characterization, as well as crop water status, are based on more complex interaction between plant and sensor, making them more difficult to implement in the field scale and more complex to interpret. This paper presents a review of these sensing technologies and discusses how they are used for precision agriculture and crop management, especially for specialty crops. Some of the challenges and considerations on the use of these sensors and technologies for specialty crop production are also discussed.
Rodriguez, L. F.; Li, C.; Khanna, M.; Spaulding, A. D.; Lin, T.; Eckhoff, S. R.
An engineering and economic evaluation of quick germ-quick fiber process for dry-grind ethanol facilities: analysis Journal Article
In: Bioresource Technology, 101(14), 5282-5289, 2010.
Abstract | Links | BibTeX | Tags:
@article{Rodriguez2010,
title = {An engineering and economic evaluation of quick germ-quick fiber process for dry-grind ethanol facilities: analysis},
author = {L.F. Rodriguez and C. Li and M. Khanna and A.D. Spaulding and T. Lin and S.R. Eckhoff},
doi = {10.1016/j.biortech.2010.01.140},
year = {2010},
date = {2010-03-06},
journal = {Bioresource Technology, 101(14), 5282-5289},
abstract = {An engineering economic model, which is mass balanced and compositionally driven, was developed to compare the conventional corn dry-grind process and the pre-fractionation process called quick germ-quick fiber (QQ). In this model, documented in a companion article, the distillers dried grains with solubles (DDGS) price was linked with its protein and fiber content as well as with the long-term average relationship with the corn price. The detailed economic analysis showed that the QQ plant retrofitted from conventional dry-grind ethanol plant reduces the manufacturing cost of ethanol by 13.5 cent/gallon and has net present value of nearly $4 million greater than the conventional dry-grind plant at an interest rate of 4% in 15years. Ethanol and feedstock price sensitivity analysis showed that the QQ plant gains more profits when ethanol price increases than conventional dry-grind ethanol plant. An optimistic analysis of the QQ process suggests that the greater value of the modified DDGS would provide greater resistance to fluctuations in corn price for QQ facilities. This model can be used to provide decision support for ethanol producers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An engineering economic model, which is mass balanced and compositionally driven, was developed to compare the conventional corn dry-grind process and the pre-fractionation process called quick germ-quick fiber (QQ). In this model, documented in a companion article, the distillers dried grains with solubles (DDGS) price was linked with its protein and fiber content as well as with the long-term average relationship with the corn price. The detailed economic analysis showed that the QQ plant retrofitted from conventional dry-grind ethanol plant reduces the manufacturing cost of ethanol by 13.5 cent/gallon and has net present value of nearly $4 million greater than the conventional dry-grind plant at an interest rate of 4% in 15years. Ethanol and feedstock price sensitivity analysis showed that the QQ plant gains more profits when ethanol price increases than conventional dry-grind ethanol plant. An optimistic analysis of the QQ process suggests that the greater value of the modified DDGS would provide greater resistance to fluctuations in corn price for QQ facilities. This model can be used to provide decision support for ethanol producers.
Li, C.; Rodriguez, L. F.; Khanna, M.; Spaulding, A. D.; Lin, T.; Eckhoff, S. R.
An engineering and economic evaluation of quick germ quick fiber process for dry-grind ethanol facilities: model description and documentation Journal Article
In: Bioresource Technology, 101(14), 5275-5281, 2010.
Abstract | Links | BibTeX | Tags:
@article{Li2010,
title = {An engineering and economic evaluation of quick germ quick fiber process for dry-grind ethanol facilities: model description and documentation},
author = {C. Li and L.F. Rodriguez and M. Khanna and A.D. Spaulding and T. Lin and S.R. Eckhoff},
doi = {10.1016/j.biortech.2010.01.139},
year = {2010},
date = {2010-01-28},
journal = {Bioresource Technology, 101(14), 5275-5281},
abstract = {An engineering economic model, which is mass balanced and compositionally driven, was developed to compare the conventional corn dry-grind process and the pre-fractionation process called “Quick germ/Quick fiber”. For the purposes of this model, the distillers dried grains with solubles price was correlated to its protein and fiber composition and the long-term average relationship with the corn price. This paper has been prepared to describe the development of the model and provide documentation for its use. This model can be used to provide decision support for ethanol producers considering the new emerging technologies that may provide sustainability to the business of ethanol production from corn.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
An engineering economic model, which is mass balanced and compositionally driven, was developed to compare the conventional corn dry-grind process and the pre-fractionation process called “Quick germ/Quick fiber”. For the purposes of this model, the distillers dried grains with solubles price was correlated to its protein and fiber composition and the long-term average relationship with the corn price. This paper has been prepared to describe the development of the model and provide documentation for its use. This model can be used to provide decision support for ethanol producers considering the new emerging technologies that may provide sustainability to the business of ethanol production from corn.
2009
Li, C.; Krewer, G.; Ji, P.; Scherm, H.; Kays, S. J.
Gas sensor array for blueberry fruit disease detection and classification Journal Article
In: Postharvest Biology and Technology, 55(3), 144-149, 2009.
Abstract | Links | BibTeX | Tags:
@article{Li2009,
title = {Gas sensor array for blueberry fruit disease detection and classification},
author = {C. Li and G. Krewer and P. Ji and H. Scherm and S.J. Kays},
doi = {10.1016/j.postharvbio.2009.11.004},
year = {2009},
date = {2009-11-09},
journal = {Postharvest Biology and Technology, 55(3), 144-149},
abstract = {A conducting polymer gas sensor array (electronic nose) was evaluated for detecting and classifying three common postharvest diseases of blueberry fruit: gray mold caused by Botrytis cinerea, anthracnose caused by Colletotrichum gloeosporioides, and Alternaria rot caused by Alternaria sp. Samples of ripe rabbiteye blueberries (Vaccinium virgatum cv. Brightwell) were inoculated individually with one of the three pathogens or left non-inoculated, and volatiles emanating from the fruit were assessed using the gas sensor array 6–10 d after inoculation in two separate experiments. Principal component analysis of volatile profiles revealed four distinct groups corresponding to the four inoculation treatments. MANOVA, conducted on profiles from individual assessment days or from combined data, confirmed that the four treatments were significantly different (P < 0.0001). A hierarchical cluster analysis indicated two super-clusters, i.e., control cluster (non-inoculated fruit) vs. pathogen cluster (inoculated fruit). Within the pathogen cluster, fruit infected by B. cinerea and Alternaria sp. were more similar to each other than to fruit infected by C. gloeosporioides. A linear Bayesian classifier achieved 90% overall correct classification for data from experiment 1. Tenax™ trapping of volatiles with short-path thermal desorption and quantification by gas chromatography–mass spectrometry was used to characterize volatile compounds emanated from the four groups of berries. Six compounds [styrene, 1-methyl-2-(1-methylethyl) benzene, eucalyptol, undecane, 5-methyl-2-(1-methylethyl)-2-cyclohexen-1-one, and thujopsene] were identified as contributing most in distinguishing differences in the volatiles emanating from the fruit due to infection. A canonical discriminant analysis model using the relative concentration of each of these compounds was developed and successfully classified the four categories of berries. This study underscores the potential feasibility of using a gas sensor array for blueberry postharvest quality assessment and fungal disease detection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A conducting polymer gas sensor array (electronic nose) was evaluated for detecting and classifying three common postharvest diseases of blueberry fruit: gray mold caused by Botrytis cinerea, anthracnose caused by Colletotrichum gloeosporioides, and Alternaria rot caused by Alternaria sp. Samples of ripe rabbiteye blueberries (Vaccinium virgatum cv. Brightwell) were inoculated individually with one of the three pathogens or left non-inoculated, and volatiles emanating from the fruit were assessed using the gas sensor array 6–10 d after inoculation in two separate experiments. Principal component analysis of volatile profiles revealed four distinct groups corresponding to the four inoculation treatments. MANOVA, conducted on profiles from individual assessment days or from combined data, confirmed that the four treatments were significantly different (P < 0.0001). A hierarchical cluster analysis indicated two super-clusters, i.e., control cluster (non-inoculated fruit) vs. pathogen cluster (inoculated fruit). Within the pathogen cluster, fruit infected by B. cinerea and Alternaria sp. were more similar to each other than to fruit infected by C. gloeosporioides. A linear Bayesian classifier achieved 90% overall correct classification for data from experiment 1. Tenax™ trapping of volatiles with short-path thermal desorption and quantification by gas chromatography–mass spectrometry was used to characterize volatile compounds emanated from the four groups of berries. Six compounds [styrene, 1-methyl-2-(1-methylethyl) benzene, eucalyptol, undecane, 5-methyl-2-(1-methylethyl)-2-cyclohexen-1-one, and thujopsene] were identified as contributing most in distinguishing differences in the volatiles emanating from the fruit due to infection. A canonical discriminant analysis model using the relative concentration of each of these compounds was developed and successfully classified the four categories of berries. This study underscores the potential feasibility of using a gas sensor array for blueberry postharvest quality assessment and fungal disease detection.
Li, C.; Gitaitis, R. D.; Tollner, E. W.; Sumner, P.; MacLean, D.
Onion sour skin detection using a gas sensor array and support vector machine Journal Article
In: Sensing and Instrumentation for Food Quality and Safety, 3(4), 193, 2009.
Abstract | Links | BibTeX | Tags:
@article{Li2009b,
title = {Onion sour skin detection using a gas sensor array and support vector machine},
author = {C. Li and R.D. Gitaitis and E.W. Tollner and P. Sumner and D. MacLean},
doi = {10.1007/s11694-009-9085-1},
year = {2009},
date = {2009-08-07},
journal = {Sensing and Instrumentation for Food Quality and Safety, 3(4), 193},
abstract = {Onion is a major vegetable crop in the world. However, various plant diseases, including sour skin caused by Burkholderia cepacia, pose a great threat to the onion industry by reducing shelf-life and are responsible for significant postharvest losses in both conventional and controlled atmosphere (CA) storage. This study investigated a new sensing approach to detect sour skin using a gas sensor array and the support vector machine (SVM). Sour skin infected onions were put in a concentration chamber for headspace accumulation and measured three to six days after inoculation. Principal component analysis (PCA) score plots showed two distinct clusters formed by healthy and sour skin infected onions. The MANOVA statistical test further proved the hypothesis that the responses of the gas sensor array to healthy onion bulbs and sour skin infected onion bulbs are significantly different (P < 0.0001). The support vector machine was employed for the classification model development. The study was undertaken in two phases: model training and cross-validation within the training datasets and model validation using new datasets. The performances of three feature selection schemes were compared using the trained SVM model. The classification results showed that although the six-sensor scheme (with 81% sensor reduction) had a slightly lower correct classification rate in the training phase, it significantly outperformed its counterparts in the validation phase (85% vs. 69% and 67%). This result proved that effective feature selection strategy could improve the discrimination power of the gas sensor array. This study demonstrated the feasibility of using a gas sensor array coupled with the SVM for the detection of sour skin in sweet onion bulbs. Early detection of sour skin will help reduce postharvest losses and secondary spread of bacteria in storage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Onion is a major vegetable crop in the world. However, various plant diseases, including sour skin caused by Burkholderia cepacia, pose a great threat to the onion industry by reducing shelf-life and are responsible for significant postharvest losses in both conventional and controlled atmosphere (CA) storage. This study investigated a new sensing approach to detect sour skin using a gas sensor array and the support vector machine (SVM). Sour skin infected onions were put in a concentration chamber for headspace accumulation and measured three to six days after inoculation. Principal component analysis (PCA) score plots showed two distinct clusters formed by healthy and sour skin infected onions. The MANOVA statistical test further proved the hypothesis that the responses of the gas sensor array to healthy onion bulbs and sour skin infected onion bulbs are significantly different (P < 0.0001). The support vector machine was employed for the classification model development. The study was undertaken in two phases: model training and cross-validation within the training datasets and model validation using new datasets. The performances of three feature selection schemes were compared using the trained SVM model. The classification results showed that although the six-sensor scheme (with 81% sensor reduction) had a slightly lower correct classification rate in the training phase, it significantly outperformed its counterparts in the validation phase (85% vs. 69% and 67%). This result proved that effective feature selection strategy could improve the discrimination power of the gas sensor array. This study demonstrated the feasibility of using a gas sensor array coupled with the SVM for the detection of sour skin in sweet onion bulbs. Early detection of sour skin will help reduce postharvest losses and secondary spread of bacteria in storage.
2008
Mosqueda, M. R. P.; Tollner, E. W.; Boyhan, G. E.; Li, C.; McClendon, R. W.
Simulating onion packinghouse product flow for performance evaluation and education Journal Article
In: Biosystems Engineering, 102(2), 135-142, 2008.
Abstract | Links | BibTeX | Tags:
@article{Mosqueda2008,
title = {Simulating onion packinghouse product flow for performance evaluation and education},
author = {M.R.P. Mosqueda and E.W. Tollner and G.E. Boyhan and C. Li and R. W. McClendon},
doi = {10.1016/j.biosystemseng.2008.09.021},
year = {2008},
date = {2008-09-26},
journal = {Biosystems Engineering, 102(2), 135-142},
abstract = {Lack of information on postharvest packinghouse performance hinders exploration, assessment of improvement opportunities and education possibilities. This study evaluated the sizing and inspection performance of 3 onion packinghouses and developed a discrete event simulation model to demonstrate the impact of improving these 2 performance variables on potential sales revenue generation, as part of a larger goal to develop a methodology for bringing packinghouses into the teaching and demonstration classroom via simulation. A group of 550 fresh sweet onions from the Vidalia production region in Georgia, US were obtained from 3 packinghouses for the 2-performance variable evaluation. Results indicated significant difference (p < 0.05) among the 3 packinghouses in terms of sizing error rate. The major departure from homogeneity was caused by a relatively higher fraction of incorrectly sized onions in 1 packinghouse. There was no significant difference (p > 0.05) between the packinghouses in terms of percentage rejects in the sorted Grade 1 onions. One packinghouse failed to meet the tolerance limit for defects, as specified by the US Grade Standards. Packinghouse managers were polled to discern impact, with houses responding with major management and packing line modifications.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Lack of information on postharvest packinghouse performance hinders exploration, assessment of improvement opportunities and education possibilities. This study evaluated the sizing and inspection performance of 3 onion packinghouses and developed a discrete event simulation model to demonstrate the impact of improving these 2 performance variables on potential sales revenue generation, as part of a larger goal to develop a methodology for bringing packinghouses into the teaching and demonstration classroom via simulation. A group of 550 fresh sweet onions from the Vidalia production region in Georgia, US were obtained from 3 packinghouses for the 2-performance variable evaluation. Results indicated significant difference (p < 0.05) among the 3 packinghouses in terms of sizing error rate. The major departure from homogeneity was caused by a relatively higher fraction of incorrectly sized onions in 1 packinghouse. There was no significant difference (p > 0.05) between the packinghouses in terms of percentage rejects in the sorted Grade 1 onions. One packinghouse failed to meet the tolerance limit for defects, as specified by the US Grade Standards. Packinghouse managers were polled to discern impact, with houses responding with major management and packing line modifications.
2007
Li, C.; Heinemann, P.
A comparative study of three evolutionary algorithms for a surface acoustic wave sensor wavelength selection Journal Article
In: Sensors and Actuators B: Chemical, 125(1), 311-320, 2007.
Abstract | Links | BibTeX | Tags:
@article{Li2007c,
title = {A comparative study of three evolutionary algorithms for a surface acoustic wave sensor wavelength selection},
author = {C. Li and P. Heinemann},
doi = {10.1016/j.snb.2007.02.026},
year = {2007},
date = {2007-07-16},
journal = {Sensors and Actuators B: Chemical, 125(1), 311-320},
abstract = {A surface acoustic wave sensor (the zNose™) was utilized to detect fruit defects by measuring and analyzing the volatile compounds emitted by apples. The zNose generates a spectrum with 512 wavelength values. This large number of variables not only increases the processing time, but reduces the classification accuracy due to irrelevant information and noise. In this study, three evolutionary techniques, genetic algorithms (GA), covariance matrix adaptation evolutionary strategy (CMAES), and differential evolution (DE) algorithms, were investigated to select the most relevant wavelengths and reduce data dimensionality of a surface acoustic wave sensor for apple defect detection. Three algorithms were compared for their search quality, search efficiency, and data dimensionality reduction. The whole spectrum, which spans 512 wavelength values, was divided into a different number of windows: with 16, 32 and 64 wavelength values in each window. These three different discretization schemes were tested by the three techniques. Both CMAES and DE yielded the best prediction accuracy with the 64 windows scenario, and GA produced comparable results with 32 windows and 64 windows, which were better than 16 windows. These results suggested that the finer the spectrum was discretized, the better the classification accuracy obtained. The results also showed that CMAES was the most efficient search algorithm with comparable search quality as DE. Three algorithms were further fine-tuned by adjusting their population size which influenced the search space. The parametric study was conducted only for the 64-window case. It was observed that algorithms with larger population size gave better search results. For CMAES, the average cost (classification error rate) for ten random seed runs was 0.0289 with the best search cost of 0.0263 by using twice the default population size (λ). Differential evolution (DE) produced slightly better search results but at the cost of reducing search efficiency. All three algorithms can effectively reduce data dimensionality by 50%, which in turn reduces the computation time.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A surface acoustic wave sensor (the zNose™) was utilized to detect fruit defects by measuring and analyzing the volatile compounds emitted by apples. The zNose generates a spectrum with 512 wavelength values. This large number of variables not only increases the processing time, but reduces the classification accuracy due to irrelevant information and noise. In this study, three evolutionary techniques, genetic algorithms (GA), covariance matrix adaptation evolutionary strategy (CMAES), and differential evolution (DE) algorithms, were investigated to select the most relevant wavelengths and reduce data dimensionality of a surface acoustic wave sensor for apple defect detection. Three algorithms were compared for their search quality, search efficiency, and data dimensionality reduction. The whole spectrum, which spans 512 wavelength values, was divided into a different number of windows: with 16, 32 and 64 wavelength values in each window. These three different discretization schemes were tested by the three techniques. Both CMAES and DE yielded the best prediction accuracy with the 64 windows scenario, and GA produced comparable results with 32 windows and 64 windows, which were better than 16 windows. These results suggested that the finer the spectrum was discretized, the better the classification accuracy obtained. The results also showed that CMAES was the most efficient search algorithm with comparable search quality as DE. Three algorithms were further fine-tuned by adjusting their population size which influenced the search space. The parametric study was conducted only for the 64-window case. It was observed that algorithms with larger population size gave better search results. For CMAES, the average cost (classification error rate) for ten random seed runs was 0.0289 with the best search cost of 0.0263 by using twice the default population size (λ). Differential evolution (DE) produced slightly better search results but at the cost of reducing search efficiency. All three algorithms can effectively reduce data dimensionality by 50%, which in turn reduces the computation time.
Li, C.; Heinemann, P.
ANN integrated electronic nose system for apple quality evaluation Journal Article
In: Transactions of the ASABE, 50(6), 2285-2294, 2007.
Abstract | Links | BibTeX | Tags: machine learning
@article{Li2007,
title = {ANN integrated electronic nose system for apple quality evaluation},
author = {C. Li and P. Heinemann},
doi = {10.13031/2013.24081},
year = {2007},
date = {2007-07-12},
urldate = {2007-07-12},
journal = {Transactions of the ASABE, 50(6), 2285-2294},
abstract = {The fresh produce industry generates more than one billion dollars each year in the U.S. market. However, fresh produce departments in grocery stores experience as much as 10% loss because the apples contain undetected defects and deteriorate in quality before they can be sold. Apple defects can create sites for pathogen development, which can cause foodborne illness. It is important to develop a non-destructive system for rapid detection and classification of defective fresh produce. In this study, an artificial neural network (ANN) based electronic nose and zNoseTM system was developed to detect physically damaged apples. Principal component analysis was used for clustering plot and feature extraction. The first five principal components were selected for the electronic nose data input, and the first ten principal components were selected for the zNoseTM spectrum data. Different ANN models, back-propagation networks (BP), probabilistic neural networks (PNN), and learning vector quantification networks (LVQ), were built and compared based on their classification accuracy, sensitivity and specificity, generalization, and incremental learning performance. For the Enose data, the BP and PNN classification rate of 85.3% and 85.1%, respectively, was better than the LVQ classification rate of 73.7%; for the zNoseTM data, the three ANN models had similar performances, which were less favorable than the Enose, with classification rates of 77%, 76.8% and 74.3%. The three ANN models' performances were also measured by their sensitivity, specificity, generalization, and incremental learning.},
keywords = {machine learning},
pubstate = {published},
tppubtype = {article}
}
The fresh produce industry generates more than one billion dollars each year in the U.S. market. However, fresh produce departments in grocery stores experience as much as 10% loss because the apples contain undetected defects and deteriorate in quality before they can be sold. Apple defects can create sites for pathogen development, which can cause foodborne illness. It is important to develop a non-destructive system for rapid detection and classification of defective fresh produce. In this study, an artificial neural network (ANN) based electronic nose and zNoseTM system was developed to detect physically damaged apples. Principal component analysis was used for clustering plot and feature extraction. The first five principal components were selected for the electronic nose data input, and the first ten principal components were selected for the zNoseTM spectrum data. Different ANN models, back-propagation networks (BP), probabilistic neural networks (PNN), and learning vector quantification networks (LVQ), were built and compared based on their classification accuracy, sensitivity and specificity, generalization, and incremental learning performance. For the Enose data, the BP and PNN classification rate of 85.3% and 85.1%, respectively, was better than the LVQ classification rate of 73.7%; for the zNoseTM data, the three ANN models had similar performances, which were less favorable than the Enose, with classification rates of 77%, 76.8% and 74.3%. The three ANN models' performances were also measured by their sensitivity, specificity, generalization, and incremental learning.
Li, C.; Heinemann, P.; Sherry, R.
Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection Journal Article
In: Sensors and Actuators B: Chemical, 125(1), 301-310, 2007.
Abstract | Links | BibTeX | Tags: machine learning
@article{Li2007b,
title = {Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection},
author = {C. Li and P. Heinemann and R. Sherry},
doi = {10.1016/j.snb.2007.02.027},
year = {2007},
date = {2007-02-26},
urldate = {2007-02-26},
journal = {Sensors and Actuators B: Chemical, 125(1), 301-310},
abstract = {The Cyranose 320 electronic nose (Enose) and zNose™ are two instruments used to detect volatile profiles. In this research, feature level and decision level multisensor data fusion models, combined with covariance matrix adaptation evolutionary strategy (CMAES), were developed to fuse the Enose and zNose data to improve detection and classification performance for damaged apples compared with using the individual instruments alone. Principal component analysis (PCA) was used for feature extraction and probabilistic neural networks (PNN) were developed as the classifier. Three feature-based fusion schemes were compared. Dynamic selective fusion achieved an average 1.8% and a best 0% classification error rate in a total of 30 independent runs. The static selective fusion approach resulted in a 6.1% classification error rate, which was not as good as using individual sensors (4.2% for the Enose and 2.6% for the zNose) if only selected features were applied. Simply adding the Enose and zNose features without selection (non-selective fusion) worsened the classification performance with a 32.5% classification error rate. This indicated that the feature selection using the CMAES is an indispensable process in multisensor data fusion, especially if multiple sources of sensors contain much irrelevant or redundant information. At the decision level, Bayesian network fusion achieved better performance than two individual sensors, with 11% error rate versus 13% error rate for the Enose and 20% error rate for the zNose. It is shown that both the feature level fusion with the CMAES optimization algorithms and decision level fusion using a Bayesian network as a classifier improved system classification performance. This methodology can also be applied to other sensor fusion applications.},
keywords = {machine learning},
pubstate = {published},
tppubtype = {article}
}
The Cyranose 320 electronic nose (Enose) and zNose™ are two instruments used to detect volatile profiles. In this research, feature level and decision level multisensor data fusion models, combined with covariance matrix adaptation evolutionary strategy (CMAES), were developed to fuse the Enose and zNose data to improve detection and classification performance for damaged apples compared with using the individual instruments alone. Principal component analysis (PCA) was used for feature extraction and probabilistic neural networks (PNN) were developed as the classifier. Three feature-based fusion schemes were compared. Dynamic selective fusion achieved an average 1.8% and a best 0% classification error rate in a total of 30 independent runs. The static selective fusion approach resulted in a 6.1% classification error rate, which was not as good as using individual sensors (4.2% for the Enose and 2.6% for the zNose) if only selected features were applied. Simply adding the Enose and zNose features without selection (non-selective fusion) worsened the classification performance with a 32.5% classification error rate. This indicated that the feature selection using the CMAES is an indispensable process in multisensor data fusion, especially if multiple sources of sensors contain much irrelevant or redundant information. At the decision level, Bayesian network fusion achieved better performance than two individual sensors, with 11% error rate versus 13% error rate for the Enose and 20% error rate for the zNose. It is shown that both the feature level fusion with the CMAES optimization algorithms and decision level fusion using a Bayesian network as a classifier improved system classification performance. This methodology can also be applied to other sensor fusion applications.
2006
Li, C.; Heinemann, P.; Reed, P.
Using genetic algorithms (GAs) and CMA evolutionary strategy to optimize electronic nose sensor selection Journal Article
In: Transactions of the ASABE, 51(1), 321-330, 2006.
Abstract | Links | BibTeX | Tags: machine learning
@article{Li2006,
title = {Using genetic algorithms (GAs) and CMA evolutionary strategy to optimize electronic nose sensor selection},
author = {C. Li and P. Heinemann and P. Reed},
doi = {10.13031/2013.21505},
year = {2006},
date = {2006-07-06},
urldate = {2006-07-06},
journal = {Transactions of the ASABE, 51(1), 321-330},
abstract = {The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, improve classification accuracy, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose, which was used for apple defect detection, was optimized by selecting only the most relevant of its internal 32 sensors using different selection methods. Two robust heuristic optimization algorithms, genetic algorithm (GA) and covariance matrix adaptation evolutionary strategy (CMAES), were applied and compared. Although both algorithms searched the optimal sensors resulting in a best classification error rate of 4.4%, the average classification error rate of CMA over 30 random seed runs was 5.0% (s.d.=0.006) which was better than 5.2% (s.d.=0.004) from the GA. The final optimal solution sets obtained by integer GA showed that including more sensors did not guarantee better classification performance. The best reduction in classification error rate was 10% while the number of sensors was reduced 78%. This study provided a robust and efficient optimization approach to reduce high data dimensionality of the electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.},
keywords = {machine learning},
pubstate = {published},
tppubtype = {article}
}
The high dimensionality of electronic nose data increases the difficulty of their use in classification models. Reducing this high dimensionality helps reduce variable numbers, improve classification accuracy, and reduce computation time and sensor cost. In this research, the Cyranose 320 electronic nose, which was used for apple defect detection, was optimized by selecting only the most relevant of its internal 32 sensors using different selection methods. Two robust heuristic optimization algorithms, genetic algorithm (GA) and covariance matrix adaptation evolutionary strategy (CMAES), were applied and compared. Although both algorithms searched the optimal sensors resulting in a best classification error rate of 4.4%, the average classification error rate of CMA over 30 random seed runs was 5.0% (s.d.=0.006) which was better than 5.2% (s.d.=0.004) from the GA. The final optimal solution sets obtained by integer GA showed that including more sensors did not guarantee better classification performance. The best reduction in classification error rate was 10% while the number of sensors was reduced 78%. This study provided a robust and efficient optimization approach to reduce high data dimensionality of the electronic nose data, which substantially improved electronic nose performance in apple defect detection while potentially reducing the overall electronic nose cost for future specific applications.
2005
Li, C.; Heinemann, P.; Irudayaraj, J.
Detection of apple defects using an electronic nose and zNose Journal Article
In: Transactions of the ASABE, 2005.
Abstract | Links | BibTeX | Tags:
@article{Li2005,
title = {Detection of apple defects using an electronic nose and zNose},
author = {C. Li and P. Heinemann and J. Irudayaraj},
doi = {10.13031/2013.19543},
year = {2005},
date = {2005-07-08},
journal = {Transactions of the ASABE},
abstract = {Apple defects and spoilage not only reduce commodity economic value, but cause food safety concerns as well. It is essential for fruit quality assurance and safety to rapidly detect fruit physical damage and spoilage. This article presents the application of an electronic nose (Cyranose 320) and zNose to the development of a nondestructive, rapid and cost effective system for the detection of defects of apples. The key compounds associated with apple aroma were identified and the “smellprints” of these key compounds were established by the electronic nose and zNose. Healthy and damaged apples were kept in 2L glass jars for 6 hours for preconcentration before measuring. Principal Component Analysis (PCA) models were developed based on the Enose and zNose data. Maholanobis distance was applied for discriminant analysis. Experiments showed that the Enose and zNose are both capable of detecting the volatile differences between healthy apples and damaged apples. After five days deterioration, the correct classification rate for the Enose was 83.3%, and for the zNose was 100%. After seven days, the correct classification rate was 100% for both instruments. For the next stage, a non-linear model and sensor fusion technique will be developed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Apple defects and spoilage not only reduce commodity economic value, but cause food safety concerns as well. It is essential for fruit quality assurance and safety to rapidly detect fruit physical damage and spoilage. This article presents the application of an electronic nose (Cyranose 320) and zNose to the development of a nondestructive, rapid and cost effective system for the detection of defects of apples. The key compounds associated with apple aroma were identified and the “smellprints” of these key compounds were established by the electronic nose and zNose. Healthy and damaged apples were kept in 2L glass jars for 6 hours for preconcentration before measuring. Principal Component Analysis (PCA) models were developed based on the Enose and zNose data. Maholanobis distance was applied for discriminant analysis. Experiments showed that the Enose and zNose are both capable of detecting the volatile differences between healthy apples and damaged apples. After five days deterioration, the correct classification rate for the Enose was 83.3%, and for the zNose was 100%. After seven days, the correct classification rate was 100% for both instruments. For the next stage, a non-linear model and sensor fusion technique will be developed.
Wu, C.; Teng, G.; Li, C.
Application and validation of computer vision based nondestructive measurement system for cucumber seedling growth conditions Journal Article
In: Transactions of the Chinese Society of Agricultural Engineering, 4, 024, 2005.
@article{Wu2005,
title = {Application and validation of computer vision based nondestructive measurement system for cucumber seedling growth conditions},
author = {C. Wu and G. Teng and C. Li},
year = {2005},
date = {2005-04-07},
journal = {Transactions of the Chinese Society of Agricultural Engineering, 4, 024},
abstract = {The possibility of using computer vision technology in greenhouse to monitor the individual cucumber plant growth conditions was studied. Destructive measurement of leaf area and dry weight and fresh weight of plant seedlings and computer vision based nondestructive measurement of these factors were compared and the correlation analysis was made. The R square value between top projected leaf area measured by computer vision and by laser leaf area meter is 0.976, and the ones between top projected leaf area and dry weight and fresh weight of individual plant are 0.874 and 0.914, respectively. The experiment shows that computer vision technology can make a relative accurate prediction of plant growth parameters.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The possibility of using computer vision technology in greenhouse to monitor the individual cucumber plant growth conditions was studied. Destructive measurement of leaf area and dry weight and fresh weight of plant seedlings and computer vision based nondestructive measurement of these factors were compared and the correlation analysis was made. The R square value between top projected leaf area measured by computer vision and by laser leaf area meter is 0.976, and the ones between top projected leaf area and dry weight and fresh weight of individual plant are 0.874 and 0.914, respectively. The experiment shows that computer vision technology can make a relative accurate prediction of plant growth parameters.
2003
Li, C.; Teng, G.; Zhao, C.; Qiao, X.; Wu, C.
Implementation of non-contact measurement of the plant growth in greenhouse using computer vision Journal Article
In: Transactions of Chinese Society of Agricultural Engineers, 2003.
BibTeX | Tags:
@article{Li2003,
title = {Implementation of non-contact measurement of the plant growth in greenhouse using computer vision},
author = {C. Li and G. Teng and C. Zhao and X. Qiao and C. Wu},
year = {2003},
date = {2003-09-09},
journal = {Transactions of Chinese Society of Agricultural Engineers},
keywords = {},
pubstate = {published},
tppubtype = {article}
}