Detection and identification of crop leaf diseases based on improved YOLOX-Nano

Objective To identify crop diseases accurately and quickly, reduce the cost of artificial diagnosis, and reduce the impacts of crop diseases on crop yield and quality.

Method Based on the analysis of the characteristics of crop diseases and spots, an improved YOLOX-Nano intelligent detection and recognition model based on convolution attention mechanism was proposed. The model employed CSPDarkNet as the backbone network, added convolutional attention module CBAM to the feature pyramid network (FPN) of the YOLOX-Nano network structure, and then introduced the mixup data enhancement method in the training. At the same time, the classification loss function was replaced by the binary cross entropy loss function (BCE Loss) with the focus loss function, the regression loss function of GIOU Loss was replaced by the CenterIOU Loss function designed in this paper, and a transfer learning strategy was also used to train the modified YOLOX-Nano model so as to improve the accuracy of crop disease detection.

Result The improved YOLOX-Nano model had parameters of 0.98×10⁶, and the detection time of a single sheet was about 0.187 s at the mobile end, with a mean average precision of 99.56%. The practical results of introducing this method into mobile terminal deployment showed that it could quickly and effectively identify common diseases of crops such as apples, corns, grapes, strawberries, potatoes and tomatoes, and achieve the balance of accuracy and speed.

Conclusion The improved model not only has higher accuracy and detection speed for crop leaf disease identification, but also has less parameters and calculation amount. The model was easy to be deployed on mobile devices such as mobile phones. In addition, the model achieves accurate positioning and identification of a variety of crop diseases in complex field environment, which is of great practical significance to guide the prevention and control of early crop diseases.