Detection and Identification of Maize Disease and Insect Pests Based on the Spatial Residual Shrinkage Network Model

The growth of corn is often affected by diseases and insect pests, which affects the yield and quality. As traditional observation methods are inefficient and disturbed by subjective factors, this study proposes a maize disease and insect pest detection system based on spatial residual network. Using image processing and deep learning techniques to quickly and accurately identify common diseases and pests, such as dwarf flower disease, gray spot, etc. The spatial residual shrinkage network model was used for feature extraction and classification, which improves the classification accuracy. The experiment shows that the average accuracy rate of the system is above 92.85, which is practical, and is expected to be widely used in the agricultural field in the future, and will help to improve the yield and quality of corn, and promote the sustainable development of agricultural production.