Abstract
As a vegetable crop with high economic value, the yield of pepper is often significantly restricted by leaf diseases, and the spots formed by these diseases on the surface of leaves are highly complex in color and texture characteristics. To overcome the shortcomings of traditional manual identification methods, such as low efficiency, time-consuming, and labor-consuming, an integrated multi-network model (IMNM) was established by combining an improved ResNet, a dynamic convolution network (DCN), and a progressive prototype network (PPN), which was aimed at five typical pepper leaf samples (healthy, virus, leaf blight, brown spot, and phyllosticta). The experimental results show that IMNM achieves 98.55% accuracy in pepper disease identification, which is significantly better than the benchmark models such as Inception-V4, ShuffleNet-V3, and EfficientNet-B7. In the cross-species generalization verification, the average identification accuracy of the model for apple, wheat, and rice leaf diseases increased to 99.81%, and its four core indicators of specificity, precision, sensitivity, and accuracy were all stable over 98%. This demonstrates that IMNM can effectively analyze the color and texture characteristics of highly heterogeneous disease spots and possesses strong cross-crop generalization capabilities. Its technical path lays a theoretical foundation for the development of field mobile disease diagnosis equipment based on deep learning, and is of great value for promoting the engineering application of an intelligent monitoring system for crop diseases and insect pests.