Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks
Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential sta...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Plant Phenome Journal |
| Online Access: | https://doi.org/10.1002/ppj2.20110 |
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| author | Emmanuel M. Gonzalez Ariyan Zarei Sebastian Calleja Clay Christenson Bruno Rozzi Jeffrey Demieville Jiahuai Hu Andrea L. Eveland Brian Dilkes Kobus Barnard Eric Lyons Duke Pauli |
| author_facet | Emmanuel M. Gonzalez Ariyan Zarei Sebastian Calleja Clay Christenson Bruno Rozzi Jeffrey Demieville Jiahuai Hu Andrea L. Eveland Brian Dilkes Kobus Barnard Eric Lyons Duke Pauli |
| author_sort | Emmanuel M. Gonzalez |
| collection | DOAJ |
| description | Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images. |
| format | Article |
| id | doaj-art-ecf9f200262f481495bf7db082804683 |
| institution | OA Journals |
| issn | 2578-2703 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Phenome Journal |
| spelling | doaj-art-ecf9f200262f481495bf7db0828046832025-08-20T01:57:21ZengWileyPlant Phenome Journal2578-27032024-12-0171n/an/a10.1002/ppj2.20110Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networksEmmanuel M. Gonzalez0Ariyan Zarei1Sebastian Calleja2Clay Christenson3Bruno Rozzi4Jeffrey Demieville5Jiahuai Hu6Andrea L. Eveland7Brian Dilkes8Kobus Barnard9Eric Lyons10Duke Pauli11School of Plant Sciences University of Arizona Tucson Arizona USADepartment of Computer Science University of Arizona Tucson Arizona USASchool of Plant Sciences University of Arizona Tucson Arizona USASchool of Plant Sciences University of Arizona Tucson Arizona USASchool of Plant Sciences University of Arizona Tucson Arizona USASchool of Plant Sciences University of Arizona Tucson Arizona USASchool of Plant Sciences University of Arizona Tucson Arizona USADonald Danforth Plant Science Center St. Louis Missouri USADepartment of Biochemistry Purdue University West Lafayette Indiana USADepartment of Computer Science University of Arizona Tucson Arizona USASchool of Plant Sciences University of Arizona Tucson Arizona USASchool of Plant Sciences University of Arizona Tucson Arizona USAAbstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.https://doi.org/10.1002/ppj2.20110 |
| spellingShingle | Emmanuel M. Gonzalez Ariyan Zarei Sebastian Calleja Clay Christenson Bruno Rozzi Jeffrey Demieville Jiahuai Hu Andrea L. Eveland Brian Dilkes Kobus Barnard Eric Lyons Duke Pauli Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks Plant Phenome Journal |
| title | Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks |
| title_full | Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks |
| title_fullStr | Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks |
| title_full_unstemmed | Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks |
| title_short | Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks |
| title_sort | quantifying leaf symptoms of sorghum charcoal rot in images of field grown plants using deep neural networks |
| url | https://doi.org/10.1002/ppj2.20110 |
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