2024
Rodriguez-Sanchez, Javier; Snider, John L.; Johnsen, Kyle; Li, Changying
Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data Journal Article
In: Frontiers in Plant Science, vol. 15, 2024, ISSN: 1664-462X.
Abstract | Links | BibTeX | Tags: agricultural robot, LiDAR, phenotyping robot, robotics
@article{10.3389/fpls.2024.1436120,
title = {Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data},
author = {Javier Rodriguez-Sanchez and John L. Snider and Kyle Johnsen and Changying Li},
url = {https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1436120},
doi = {10.3389/fpls.2024.1436120},
issn = {1664-462X},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Frontiers in Plant Science},
volume = {15},
abstract = {<p>Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird’s-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (<italic>R</italic>^{2} = 0.95},
keywords = {agricultural robot, LiDAR, phenotyping robot, robotics},
pubstate = {published},
tppubtype = {article}
}
<p>Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird’s-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (<italic>R</italic>2 = 0.95
2023
Herr, Andrew W.; Adak, Alper; Carroll, Matthew E.; Elango, Dinakaran; Kar, Soumyashree; Li, Changying; Jones, Sarah E.; Carter, Arron H.; Murray, Seth C.; Paterson, Andrew; Sankaran, Sindhuja; Singh, Arti; Singh, Asheesh K.
Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding Journal Article
In: Crop Science, vol. 63, no. 4, pp. 1722-1749, 2023.
Abstract | Links | BibTeX | Tags: agricultural robot, High-throughput phenotyping, review
@article{https://doi.org/10.1002/csc2.21028,
title = {Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding},
author = {Andrew W. Herr and Alper Adak and Matthew E. Carroll and Dinakaran Elango and Soumyashree Kar and Changying Li and Sarah E. Jones and Arron H. Carter and Seth C. Murray and Andrew Paterson and Sindhuja Sankaran and Arti Singh and Asheesh K. Singh},
url = {https://acsess.onlinelibrary.wiley.com/doi/abs/10.1002/csc2.21028},
doi = {https://doi.org/10.1002/csc2.21028},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Crop Science},
volume = {63},
number = {4},
pages = {1722-1749},
abstract = {Abstract High-throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS-collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.},
keywords = {agricultural robot, High-throughput phenotyping, review},
pubstate = {published},
tppubtype = {article}
}
Abstract High-throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of unoccupied aerial vehicles (UAV; or drones) and sensor(s), is an increasingly promising tool for plant breeders and researchers. Enthusiasm and opportunities from this technology for plant breeding are similar to the emergence of genomic tools ∼30 years ago, and genomic selection more recently. Unlike genomic tools, HTP provides a variety of strategies in implementation and utilization that generate big data on the dynamic nature of plant growth formed by temporal interactions between growth and environment. This review lays out strategies deployed across four major staple crop species: cotton (Gossypium hirsutum L.), maize (Zea mays L.), soybean (Glycine max L.), and wheat (Triticum aestivum L.). Each crop highlighted in this review demonstrates how UAS-collected data are employed to automate and improve estimation or prediction of objective phenotypic traits. Each crop section includes four major topics: (a) phenotyping of routine traits, (b) phenotyping of previously infeasible traits, (c) sample cases of UAS application in breeding, and (d) implementation of phenotypic and phenomic prediction and selection. While phenotyping of routine agronomic and productivity traits brings advantages in time and resource optimization, the most potentially beneficial application of UAS data is in collecting traits that were previously difficult or impossible to quantify, improving selection efficiency of important phenotypes. In brief, UAS sensor technology can be used for measuring abiotic stress, biotic stress, crop growth and development, as well as productivity. These applications and the potential implementation of machine learning strategies allow for improved prediction, selection, and efficiency within breeding programs, making UAS HTP a potentially indispensable asset.
2022
Xu, Rui; Li, Changying
A review of field-based high-throughput phenotyping systems: focusing on ground robots Journal Article
In: Plant Phenomics, vol. 2022, no. Article ID 9760269, pp. 20, 2022.
Links | BibTeX | Tags: agricultural robot, High-throughput phenotyping, phenotyping robot, review, robotics
@article{nokey,
title = {A review of field-based high-throughput phenotyping systems: focusing on ground robots},
author = {Rui Xu and Changying Li},
url = {https://spj.sciencemag.org/journals/plantphenomics/2022/9760269/},
doi = {https://doi.org/10.34133/2022/9760269.},
year = {2022},
date = {2022-06-18},
urldate = {2022-06-18},
journal = {Plant Phenomics},
volume = {2022},
number = {Article ID 9760269},
pages = {20},
keywords = {agricultural robot, High-throughput phenotyping, phenotyping robot, review, robotics},
pubstate = {published},
tppubtype = {article}
}
Xu, Rui; Li, Changying
A modular agricultural robotic system (MARS) for precision farming: Concept and implementation Journal Article
In: Journal of Field Robotics, vol. 39, no. 4, pp. 387-409, 2022.
Abstract | Links | BibTeX | Tags: agricultural robot, High-throughput phenotyping, phenotyping robot
@article{https://doi.org/10.1002/rob.22056,
title = {A modular agricultural robotic system (MARS) for precision farming: Concept and implementation},
author = {Rui Xu and Changying Li},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22056},
doi = {https://doi.org/10.1002/rob.22056},
year = {2022},
date = {2022-01-01},
journal = {Journal of Field Robotics},
volume = {39},
number = {4},
pages = {387-409},
abstract = {Abstract Increasing global population, climate change, and shortage of labor pose significant challenges for meeting the global food and fiber demand, and agricultural robots offer a promising solution to these challenges. This paper presents a new robotic system architecture and the resulting modular agricultural robotic system (MARS) that is an autonomous, multi-purpose, and affordable robotic platform for in-field plant high throughput phenotyping and precision farming. There are five essential hardware modules (wheel module, connection module, robot controller, robot frame, and power module) and three optional hardware modules (actuation module, sensing module, and smart attachment). Various combinations of the hardware modules can create different robot configurations for specific agricultural tasks. The software was designed using the Robot Operating System (ROS) with three modules: control module, navigation module, and vision module. A robot localization method using dual Global Navigation Satellite System antennas was developed. Two line-following algorithms were implemented as the local planner for the ROS navigation stack. Based on the MARS design concept, two MARS designs were implemented: a low-cost, lightweight robotic system named MARS mini and a heavy-duty robot named MARS X. The autonomous navigation of both MARS X and mini was evaluated at different traveling speeds and payload levels, confirming satisfactory performances. The MARS X was further tested for its performance and navigation accuracy in a crop field, achieving a high accuracy over a 537 m long path with only 15% of the path having an error larger than 0.05 m. The MARS mini and MARS X were shown to be useful for plant phenotyping in two field tests. The modular design makes the robots easily adaptable to different agricultural tasks and the low-cost feature makes it affordable for researchers and growers.},
keywords = {agricultural robot, High-throughput phenotyping, phenotyping robot},
pubstate = {published},
tppubtype = {article}
}
Abstract Increasing global population, climate change, and shortage of labor pose significant challenges for meeting the global food and fiber demand, and agricultural robots offer a promising solution to these challenges. This paper presents a new robotic system architecture and the resulting modular agricultural robotic system (MARS) that is an autonomous, multi-purpose, and affordable robotic platform for in-field plant high throughput phenotyping and precision farming. There are five essential hardware modules (wheel module, connection module, robot controller, robot frame, and power module) and three optional hardware modules (actuation module, sensing module, and smart attachment). Various combinations of the hardware modules can create different robot configurations for specific agricultural tasks. The software was designed using the Robot Operating System (ROS) with three modules: control module, navigation module, and vision module. A robot localization method using dual Global Navigation Satellite System antennas was developed. Two line-following algorithms were implemented as the local planner for the ROS navigation stack. Based on the MARS design concept, two MARS designs were implemented: a low-cost, lightweight robotic system named MARS mini and a heavy-duty robot named MARS X. The autonomous navigation of both MARS X and mini was evaluated at different traveling speeds and payload levels, confirming satisfactory performances. The MARS X was further tested for its performance and navigation accuracy in a crop field, achieving a high accuracy over a 537 m long path with only 15% of the path having an error larger than 0.05 m. The MARS mini and MARS X were shown to be useful for plant phenotyping in two field tests. The modular design makes the robots easily adaptable to different agricultural tasks and the low-cost feature makes it affordable for researchers and growers.
2021
Xu, Rui; Li, Changying; Bernardes, Sergio
Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture Journal Article
In: Remote Sensing, vol. 13, no. 17, 2021, ISSN: 2072-4292.
Abstract | Links | BibTeX | Tags: agricultural robot, High-throughput phenotyping, robotics
@article{Xu2021,
title = {Development and Testing of a UAV-Based Multi-Sensor System for Plant Phenotyping and Precision Agriculture},
author = {Rui Xu and Changying Li and Sergio Bernardes},
url = {https://www.mdpi.com/2072-4292/13/17/3517},
doi = {10.3390/rs13173517},
issn = {2072-4292},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Remote Sensing},
volume = {13},
number = {17},
abstract = {Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.},
keywords = {agricultural robot, High-throughput phenotyping, robotics},
pubstate = {published},
tppubtype = {article}
}
Unmanned aerial vehicles have been used widely in plant phenotyping and precision agriculture. Several critical challenges remain, however, such as the lack of cross-platform data acquisition software system, sensor calibration protocols, and data processing methods. This paper developed an unmanned aerial system that integrates three cameras (RGB, multispectral, and thermal) and a LiDAR sensor. Data acquisition software supporting data recording and visualization was implemented to run on the Robot Operating System. The design of the multi-sensor unmanned aerial system was open sourced. A data processing pipeline was proposed to preprocess the raw data and to extract phenotypic traits at the plot level, including morphological traits (canopy height, canopy cover, and canopy volume), canopy vegetation index, and canopy temperature. Protocols for both field and laboratory calibrations were developed for the RGB, multispectral, and thermal cameras. The system was validated using ground data collected in a cotton field. Temperatures derived from thermal images had a mean absolute error of 1.02 °C, and canopy NDVI had a mean relative error of 6.6% compared to ground measurements. The observed error for maximum canopy height was 0.1 m. The results show that the system can be useful for plant breeding and precision crop management.
2020
Iqbal, Jawad; Xu, Rui; Halloran, Hunter; Li, Changying
Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing Journal Article
In: Electronics, vol. 9, no. 9, pp. 1550, 2020.
Links | BibTeX | Tags: agricultural robot, mobile, phenotyping robot, robotics
@article{iqbal2020maria,
title = {Development of a Multi-Purpose Autonomous Differential Drive Mobile Robot for Plant Phenotyping and Soil Sensing},
author = {Jawad Iqbal and Rui Xu and Hunter Halloran and Changying Li },
url = {https://www.mdpi.com/2079-9292/9/9/1550},
year = {2020},
date = {2020-09-15},
urldate = {2020-09-15},
journal = {Electronics},
volume = {9},
number = {9},
pages = {1550},
keywords = {agricultural robot, mobile, phenotyping robot, robotics},
pubstate = {published},
tppubtype = {article}
}
2019
Xu, R.; Li, C.; Paterson, A. H.
Multispectral imaging and unmanned aerial systems for cotton plant phenotyping Journal Article
In: PLoS One, no. 0205083, 2019.
Abstract | Links | BibTeX | Tags: agricultural robot, robotics
@article{Xu201901,
title = {Multispectral imaging and unmanned aerial systems for cotton plant phenotyping},
author = {R. Xu and C. Li and A.H. Paterson},
url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/Multispectral-imaging-and-unmanned-aerial-systems-for-cotton-plant-phenotyping.pdf},
doi = {https://doi.org/10.1371/journal.pone.0205083},
year = {2019},
date = {2019-02-27},
urldate = {2019-02-27},
journal = {PLoS One},
number = {0205083},
abstract = {Xu, R., Li, C., & Paterson, A. H. (2019). Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PloS one, 14(2), e0205083.
Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations (R2 = 0.88–0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations (R2 = 0.78–0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management.},
keywords = {agricultural robot, robotics},
pubstate = {published},
tppubtype = {article}
}
Xu, R., Li, C., & Paterson, A. H. (2019). Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PloS one, 14(2), e0205083.
Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations (R2 = 0.88–0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations (R2 = 0.78–0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management.
Size and shape are important properties of shrub crops such as blueberries, and they can be particularly useful for evaluating bush architecture suited to mechanical harvesting. The overall goal of this study was to develop a 3D imaging approach to measure size-related traits and bush shape that are relevant to mechanical harvesting. 3D point clouds were acquired for 367 bushes from five genotype groups. Point cloud data were preprocessed to obtain clean bush points for characterizing bush architecture, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). One-dimensional traits (height, width, and crown size) had high correlations (R2 = 0.88–0.95) between proposed method and manual measurements, whereas bush volume showed relatively lower correlations (R2 = 0.78–0.85). These correlations suggested that the present approach was accurate in measuring one-dimensional size traits and acceptable in estimating three-dimensional bush volume. Statistical results demonstrated that the five genotype groups were statistically different in crown size and bush shape. The differences matched with human evaluation regarding optimal bush architecture for mechanical harvesting. In particular, a visualization tool could be generated using crown size and path curve λ, which showed great potential of determining bush architecture suitable for mechanical harvesting quickly. Therefore, the processing pipeline of 3D point cloud data presented in this study is an effective tool for blueberry breeding programs (in particular for mechanical harvesting) and farm management.
2017
Patrick, A.; Li, C.
High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems Journal Article
In: Remote Sensing, 9(12), 1250, 2017.
Abstract | Links | BibTeX | Tags: agricultural robot, High-throughput phenotyping, phenotyping robot, robotics
@article{Patrick2017,
title = {High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems},
author = {A. Patrick and C. Li
},
url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/High-Throughput-Phenotyping-of-Blueberry-Bush-Morphological-Traits-Using-Unmanned-Aerial-Systems.pdf},
doi = {10.3390/rs9121250},
year = {2017},
date = {2017-11-30},
urldate = {2017-11-30},
journal = {Remote Sensing, 9(12), 1250},
abstract = {Patrick, A., & Li, C. (2017). High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote Sensing, 9(12), 1250.
Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions. This paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes. Color images collected by a quadcopter are processed into three-dimensional point clouds via structure from motion algorithms. Bush height, extents, canopy area, and volume, in addition to crown diameter and width, are derived and referenced to ground truth. In an experimental farm, twenty-five bushes were imaged by a quadcopter. Height and width dimensions achieved a mean absolute error of 9.85 cm before and 5.82 cm after systematic under-estimation correction. Strong correlation was found between manual and image derived bush volumes and their traditional growth indices. Hedgerows of three Southern Highbush varieties were imaged at a commercial farm to extract five morphological features (base angle, blockiness, crown percent height, crown ratio, and vegetation ratio) associated with cultivation and machine harvestability. The bushes were found to be partially separable by multivariate analysis. The methodology developed from this study is not only valuable for plant breeders to screen genotypes with bush morphological traits that are suitable for machine harvest, but can also aid producers in crop management such as pruning and plot layout organization.},
keywords = {agricultural robot, High-throughput phenotyping, phenotyping robot, robotics},
pubstate = {published},
tppubtype = {article}
}
Patrick, A., & Li, C. (2017). High throughput phenotyping of blueberry bush morphological traits using unmanned aerial systems. Remote Sensing, 9(12), 1250.
Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions. This paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes. Color images collected by a quadcopter are processed into three-dimensional point clouds via structure from motion algorithms. Bush height, extents, canopy area, and volume, in addition to crown diameter and width, are derived and referenced to ground truth. In an experimental farm, twenty-five bushes were imaged by a quadcopter. Height and width dimensions achieved a mean absolute error of 9.85 cm before and 5.82 cm after systematic under-estimation correction. Strong correlation was found between manual and image derived bush volumes and their traditional growth indices. Hedgerows of three Southern Highbush varieties were imaged at a commercial farm to extract five morphological features (base angle, blockiness, crown percent height, crown ratio, and vegetation ratio) associated with cultivation and machine harvestability. The bushes were found to be partially separable by multivariate analysis. The methodology developed from this study is not only valuable for plant breeders to screen genotypes with bush morphological traits that are suitable for machine harvest, but can also aid producers in crop management such as pruning and plot layout organization.
Phenotyping morphological traits of blueberry bushes in the field is important for selecting genotypes that are easily harvested by mechanical harvesters. Morphological data can also be used to assess the effects of crop treatments such as plant growth regulators, fertilizers, and environmental conditions. This paper investigates the feasibility and accuracy of an inexpensive unmanned aerial system in determining the morphological characteristics of blueberry bushes. Color images collected by a quadcopter are processed into three-dimensional point clouds via structure from motion algorithms. Bush height, extents, canopy area, and volume, in addition to crown diameter and width, are derived and referenced to ground truth. In an experimental farm, twenty-five bushes were imaged by a quadcopter. Height and width dimensions achieved a mean absolute error of 9.85 cm before and 5.82 cm after systematic under-estimation correction. Strong correlation was found between manual and image derived bush volumes and their traditional growth indices. Hedgerows of three Southern Highbush varieties were imaged at a commercial farm to extract five morphological features (base angle, blockiness, crown percent height, crown ratio, and vegetation ratio) associated with cultivation and machine harvestability. The bushes were found to be partially separable by multivariate analysis. The methodology developed from this study is not only valuable for plant breeders to screen genotypes with bush morphological traits that are suitable for machine harvest, but can also aid producers in crop management such as pruning and plot layout organization.
Patrick, A.; Pelham, S.; Culbreath, A.; Holbrook, C.; Godoy, I. J. d.; Li, C.
High Throughput Phenotyping of Tomato Spot Wilt Disease in Peanuts Using Unmanned Aerial Systems and Multispectral Imaging Journal Article
In: IEEE Instrumentation & Measurement Magazine, 20(3), 4-12, 2017.
Abstract | Links | BibTeX | Tags: agricultural robot, High-throughput phenotyping, phenotyping robot, robotics
@article{Patrick2017b,
title = {High Throughput Phenotyping of Tomato Spot Wilt Disease in Peanuts Using Unmanned Aerial Systems and Multispectral Imaging},
author = {A. Patrick and S. Pelham and A. Culbreath and C. Holbrook and I.J.d. Godoy and C. Li},
url = {http://sensinglab.engr.uga.edu//srv/htdocs/wp-content/uploads/2019/11/High-Throughput-Phenotyping-of-Tomato-Spot-Wilt-Disease-in-Peanuts-Using-Unmanned-Aerial-Systems-and-Multispectral-Imaging.pdf},
doi = {10.1109/MIM.2017.7951684},
year = {2017},
date = {2017-02-08},
urldate = {2017-02-08},
journal = {IEEE Instrumentation & Measurement Magazine, 20(3), 4-12},
abstract = {Patrick, A., Pelham, S., Culbreath, A., Holbrook, C. C., De Godoy, I. J., & Li, C. (2017). High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging. IEEE Instrumentation & Measurement Magazine, 20(3), 4-12.
The amount of visible and near infrared light reflected by plants varies depending on their health. In this study, multispectral images were acquired by a quadcopter for high throughput phenotyping of tomato spot wilt disease resistance among twenty genotypes of peanuts. The plants were visually assessed to acquire ground truth ratings of disease incidence. Multispectral images were processed into several vegetation indices. The vegetation index image of each plot has a unique distribution of pixel intensities. The percentage and number of pixels above and below varying thresholds were extracted. These features were correlated with manually acquired data to develop a model for assessing the percentage of each plot diseased. Ultimately, the best vegetation indices and pixel distribution feature for disease detection were determined and correlated with manual ratings and yield. The relative resistance of each genotype was then compared. Image-based disease ratings effectively ranked genotype resistance as early as 93 days from seeding.},
keywords = {agricultural robot, High-throughput phenotyping, phenotyping robot, robotics},
pubstate = {published},
tppubtype = {article}
}
Patrick, A., Pelham, S., Culbreath, A., Holbrook, C. C., De Godoy, I. J., & Li, C. (2017). High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging. IEEE Instrumentation & Measurement Magazine, 20(3), 4-12.
The amount of visible and near infrared light reflected by plants varies depending on their health. In this study, multispectral images were acquired by a quadcopter for high throughput phenotyping of tomato spot wilt disease resistance among twenty genotypes of peanuts. The plants were visually assessed to acquire ground truth ratings of disease incidence. Multispectral images were processed into several vegetation indices. The vegetation index image of each plot has a unique distribution of pixel intensities. The percentage and number of pixels above and below varying thresholds were extracted. These features were correlated with manually acquired data to develop a model for assessing the percentage of each plot diseased. Ultimately, the best vegetation indices and pixel distribution feature for disease detection were determined and correlated with manual ratings and yield. The relative resistance of each genotype was then compared. Image-based disease ratings effectively ranked genotype resistance as early as 93 days from seeding.
The amount of visible and near infrared light reflected by plants varies depending on their health. In this study, multispectral images were acquired by a quadcopter for high throughput phenotyping of tomato spot wilt disease resistance among twenty genotypes of peanuts. The plants were visually assessed to acquire ground truth ratings of disease incidence. Multispectral images were processed into several vegetation indices. The vegetation index image of each plot has a unique distribution of pixel intensities. The percentage and number of pixels above and below varying thresholds were extracted. These features were correlated with manually acquired data to develop a model for assessing the percentage of each plot diseased. Ultimately, the best vegetation indices and pixel distribution feature for disease detection were determined and correlated with manual ratings and yield. The relative resistance of each genotype was then compared. Image-based disease ratings effectively ranked genotype resistance as early as 93 days from seeding.