2017
Kuzy, J. D.; Jiang, Y.; Li, C.
Blueberry bruise detection by pulsed thermographic imaging Journal Article
In: Postharvest Biology and Technology, 136, 166-177, 2017.
Abstract | Links | BibTeX | Tags: thermography
@article{Kuzy2017,
title = {Blueberry bruise detection by pulsed thermographic imaging},
author = {J. D. Kuzy and Y. Jiang and C. Li},
url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Blueberry-bruise-detection-by-pulsed-thermographic-imaging.pdf},
doi = {10.1016/j.postharvbio.2017.10.011},
year = {2017},
date = {2017-10-24},
urldate = {2017-10-24},
journal = {Postharvest Biology and Technology, 136, 166-177},
abstract = {Kuzy, J., Jiang, Y., & Li, C. (2018). Blueberry bruise detection by pulsed thermographic imaging. Postharvest Biology and Technology, 136, 166-177.
Blueberries are prone to internal bruising damage during harvesting and postharvest handling. Accurate assessment of bruising damage improves profitability by allowing allocation of berries to appropriate product streams. The goal of this study was to develop a pulsed thermographic imaging system and explore its feasibility in non-destructively detecting bruised blueberries. In this paper, the design and construction of a pulsed thermographic imaging system was described. A total of 200 blueberry fruit samples from two southern highbush cultivars (Farthing and Meadowlark) were collected and bruising treatments were applied to half of the samples. Relevant features were extracted and were demonstrated to be significantly different between healthy and bruised fruit. Classification was performed using linear discriminant analysis, support vector machine, random forest, k-nearest-neighbors, and logistic regression classifiers. Accuracies of up to 88% and 79% were obtained for Farthing and Meadowlark berries, respectively. These results demonstrate the feasibility of pulsed thermography to discriminate between bruised and healthy blueberries.},
keywords = {thermography},
pubstate = {published},
tppubtype = {article}
}
Kuzy, J., Jiang, Y., & Li, C. (2018). Blueberry bruise detection by pulsed thermographic imaging. Postharvest Biology and Technology, 136, 166-177.
Blueberries are prone to internal bruising damage during harvesting and postharvest handling. Accurate assessment of bruising damage improves profitability by allowing allocation of berries to appropriate product streams. The goal of this study was to develop a pulsed thermographic imaging system and explore its feasibility in non-destructively detecting bruised blueberries. In this paper, the design and construction of a pulsed thermographic imaging system was described. A total of 200 blueberry fruit samples from two southern highbush cultivars (Farthing and Meadowlark) were collected and bruising treatments were applied to half of the samples. Relevant features were extracted and were demonstrated to be significantly different between healthy and bruised fruit. Classification was performed using linear discriminant analysis, support vector machine, random forest, k-nearest-neighbors, and logistic regression classifiers. Accuracies of up to 88% and 79% were obtained for Farthing and Meadowlark berries, respectively. These results demonstrate the feasibility of pulsed thermography to discriminate between bruised and healthy blueberries.
Blueberries are prone to internal bruising damage during harvesting and postharvest handling. Accurate assessment of bruising damage improves profitability by allowing allocation of berries to appropriate product streams. The goal of this study was to develop a pulsed thermographic imaging system and explore its feasibility in non-destructively detecting bruised blueberries. In this paper, the design and construction of a pulsed thermographic imaging system was described. A total of 200 blueberry fruit samples from two southern highbush cultivars (Farthing and Meadowlark) were collected and bruising treatments were applied to half of the samples. Relevant features were extracted and were demonstrated to be significantly different between healthy and bruised fruit. Classification was performed using linear discriminant analysis, support vector machine, random forest, k-nearest-neighbors, and logistic regression classifiers. Accuracies of up to 88% and 79% were obtained for Farthing and Meadowlark berries, respectively. These results demonstrate the feasibility of pulsed thermography to discriminate between bruised and healthy blueberries.
Kuzy, J. D.; Li, C.
A Pulsed Thermographic Imaging System for Detection and Identification of Cotton Foreign Matter Journal Article
In: Sensors, 17(3), 518, 2017.
Abstract | Links | BibTeX | Tags: thermography
@article{Kuzy2017b,
title = {A Pulsed Thermographic Imaging System for Detection and Identification of Cotton Foreign Matter },
author = {J. D. Kuzy and C. Li},
url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/A-Pulsed-Thermographic-Imaging-System-for-Detection-and-Identification-of-Cotton-Foreign-Matter.pdf},
doi = {10.3390/s17030518},
year = {2017},
date = {2017-03-02},
urldate = {2017-03-02},
journal = {Sensors, 17(3), 518},
abstract = {Kuzy, J., & Li, C. (2017). A pulsed thermographic imaging system for detection and identification of cotton foreign matter. Sensors, 17(3), 518.
Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter.},
keywords = {thermography},
pubstate = {published},
tppubtype = {article}
}
Kuzy, J., & Li, C. (2017). A pulsed thermographic imaging system for detection and identification of cotton foreign matter. Sensors, 17(3), 518.
Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter.
Detection of foreign matter in cleaned cotton is instrumental to accurately grading cotton quality, which in turn impacts the marketability of the cotton. Current grading systems return estimates of the amount of foreign matter present, but provide no information about the identity of the contaminants. This paper explores the use of pulsed thermographic analysis to detect and identify cotton foreign matter. The design and implementation of a pulsed thermographic analysis system is described. A sample set of 240 foreign matter and cotton lint samples were collected. Hand-crafted waveform features and frequency-domain features were extracted and analyzed for statistical significance. Classification was performed on these features using linear discriminant analysis and support vector machines. Using waveform features and support vector machine classifiers, detection of cotton foreign matter was performed with 99.17% accuracy. Using frequency-domain features and linear discriminant analysis, identification was performed with 90.00% accuracy. These results demonstrate that pulsed thermographic imaging analysis produces data which is of significant utility for the detection and identification of cotton foreign matter.