Rui Zhang1,2, Tonghai Liu1,2,*, Wenzheng Liu1,2, Chaungchuang Yuan1,2, Xiaoyue Seng1,2, Tiantian Guo1,2, Xue Wang1,2
Phyton-International Journal of Experimental Botany, Vol.93, No.6, pp. 1275-1296, 2024, DOI:10.32604/phyton.2024.052397
- 27 June 2024
Abstract Rice diseases can adversely affect both the yield and quality of rice crops, leading to the increased use of pesticides and environmental pollution. Accurate detection of rice diseases in natural environments is crucial for both operational efficiency and quality assurance. Deep learning-based disease identification technologies have shown promise in automatically discerning disease types. However, effectively extracting early disease features in natural environments remains a challenging problem. To address this issue, this study proposes the YOLO-CRD method. This research selected images of common rice diseases, primarily bakanae disease, bacterial brown spot, leaf rice fever, and dry… More >
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