1. Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production.
- Author
-
Bouhouch Y, Esmaeel Q, Richet N, Barka EA, Backes A, Steffenel LA, Hafidi M, Jacquard C, and Sanchez L
- Subjects
- Crop Production methods, Neural Networks, Computer, Crops, Agricultural microbiology, Hordeum microbiology, Plant Diseases microbiology, Plant Diseases prevention & control, Deep Learning, Ascomycota physiology, Plant Leaves microbiology
- Abstract
Net blotch disease caused by Drechslera teres is a major fungal disease that affects barley ( Hordeum vulgare ) plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection on seedling leaves using Cascade R-CNN (region-based convolutional neural network) and U-Net (a convolutional neural network) architectures. We used a dataset of barley leaf images with annotations of net blotch disease to train and evaluate the model. The model achieved an accuracy of 95% for Cascade R-CNN in net blotch disease detection and a Jaccard index score of 0.99, indicating high accuracy in disease quantification and location. The combination of Cascade R-CNN and U-Net architectures improved the detection of small and irregularly shaped lesions in the images at 4 days postinfection, leading to better disease quantification. To validate the model developed, we compared the results obtained by automated measurement with a classical method (necrosis diameter measurement) and a pathogen detection by real-time PCR. The proposed deep learning model could be used in automated systems for disease quantification and to screen the efficacy of potential biocontrol agents to protect against disease., Competing Interests: The author(s) declare no conflict of interest.
- Published
- 2024
- Full Text
- View/download PDF