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.