In the field of precision agriculture,diagnosing rice diseases from images remains challenging due to high error rates,multiple influencing factors,and unstable conditions.While machine learning and convolutional neur...In the field of precision agriculture,diagnosing rice diseases from images remains challenging due to high error rates,multiple influencing factors,and unstable conditions.While machine learning and convolutional neural networks have shown promising results in identifying rice diseases,they were limited in their ability to explain the relationships among disease features.In this study,we proposed an improved rice disease classification method that combines a convolutional neural network(CNN)with a bidirectional gated recurrent unit(BiGRU).Specifically,we introduced a residual mechanism into the Inception module,expanded the module's depth,and integrated an improved Convolutional Block Attention Module(CBAM).We trained and tested the improved CNN and BiGRU,concatenated the outputs of the CNN and BiGRU modules,and passed them to the classification layer for recognition.Our experiments demonstrate that this approach achieves an accuracy of 98.21%in identifying four types of rice diseases,providing a reliable method for rice disease recognition research.展开更多
基金the National Natural Science Foundation of China under Grants U21A2019,61873058 and 61933007the Hainan Province Science and Technology Special Fund under Grant ZDYF2022-SHFZ105+1 种基金Heilongjiang Natural Science Foundation of China under Grant LH2020F042the Scientific Research Starting Foundation for Post Doctor from Heilongjiang under Grant LBH-Q17134.
文摘In the field of precision agriculture,diagnosing rice diseases from images remains challenging due to high error rates,multiple influencing factors,and unstable conditions.While machine learning and convolutional neural networks have shown promising results in identifying rice diseases,they were limited in their ability to explain the relationships among disease features.In this study,we proposed an improved rice disease classification method that combines a convolutional neural network(CNN)with a bidirectional gated recurrent unit(BiGRU).Specifically,we introduced a residual mechanism into the Inception module,expanded the module's depth,and integrated an improved Convolutional Block Attention Module(CBAM).We trained and tested the improved CNN and BiGRU,concatenated the outputs of the CNN and BiGRU modules,and passed them to the classification layer for recognition.Our experiments demonstrate that this approach achieves an accuracy of 98.21%in identifying four types of rice diseases,providing a reliable method for rice disease recognition research.
