Leaf disease segmentation model of greenhouse tomatoes based on ResUnet with attention mechanism

In view of the difficulty in accurately identifying tomato early blight spot in the complex greenhouse environment, a ResUnet model of integrated channel attention mechanism was proposed. The data set of tomato early blight under complex greenhouse environment was constructed. Lesions were segmented by ResUnet model integrating channel attention mechanism, in which ResUnet network could learn the importance of different depth features, and embedding channel attention mechanism made the improved model pay more attention to the location features of lesions.The accuracy of this model was 97%, 1. 99% and 2. 97% higher than that of Unet and Resnet101 models, respectively.The parameters and weights of backbone network layer obtained from tomato early blight spot segmentation model were transferred to the single background spot segmentation model of pepper scab, apple gray spot, grape black rot, etc., and improved and the parameters were fine-tuned to achieve accurate spot segmentation. On the basis of the research algorithm, the intelligent diagnosis system is designed, which can quickly and accurately diagnose the studied crop diseases and provide basis for timely prevention and control.