Improved Identification of Leaf Diseases and Pest Infestations on Rice by Means of Coordinate Attention Mechanism-based Residual Network
[Objective]A new deep learning network was designed to improve the often-inaccurate identification of diseases and pest infestations on rice.[Method]The coordinate attention mechanism(CA)was introduced under the residual convolution block of RestNet-50 using the LeakyRelu activation function to replace the Relu activation function as well as the three 3×3 convolution kernels to replace the original 7×7 convolution kernel under the first convolution layer.[Result]The newly designed ResNet-50-CA effectively balanced the detection accuracy and model simplicity the original method lacked.The improved model was further fine-tuned with experiments to achieve a much-improved detection accuracy of 99.21%in identifying the diseases and infestations on a batch of 16 specimens with a learning rate of 0.0001.[Conclusion]The superior deep learning algorithm of the current ResNet50-CA system extracted more detailed and accurate information on the diseases and infestations than did the previous model.It could be applied for field and/or clinic diagnosis on rice plants.
Deep learning networkResNet50rice leaf diseases and pest infestationscoordinate attention mechanism
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