Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks Article Swipe
Emmanuel Gonzalez
,
Ariyan Zarei
,
Sebastian Calleja
,
C. Christenson
,
Bruno Rozzi
,
Jeffrey Demieville
,
Jiahuai Hu
,
Andrea L. Eveland
,
Brian P. Dilkes
,
Kobus Barnard
,
Eric Lyons
,
Duke Pauli
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ppj2.20110
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ppj2.20110
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ppj2.20110
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ppj2.20110
- OA Status
- gold
- Cited By
- 3
- References
- 73
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400092665
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400092665Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/ppj2.20110Digital Object Identifier
- Title
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Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-27Full publication date if available
- Authors
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Emmanuel Gonzalez, Ariyan Zarei, Sebastian Calleja, C. Christenson, Bruno Rozzi, Jeffrey Demieville, Jiahuai Hu, Andrea L. Eveland, Brian P. Dilkes, Kobus Barnard, Eric Lyons, Duke PauliList of authors in order
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https://doi.org/10.1002/ppj2.20110Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ppj2.20110Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ppj2.20110Direct OA link when available
- Concepts
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Sorghum, Artificial intelligence, Convolutional neural network, F1 score, Segmentation, Pattern recognition (psychology), Inference, Macrophomina phaseolina, Computer science, Machine learning, Agronomy, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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73Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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