Image recognition method for solving crop disease and insect pest sample imbalance problem

The invention relates to the field of disease and insect pest recognition, in particular to an image recognition method for solving the problem of crop disease and insect pest sample imbalance. The method comprises the following steps: performing model training by utilizing a current labeled data set, selecting a current optimal model through model verification, performing multiple times of image enhancement on pictures of a non-labeled data set, reasoning and screening the enhanced images to obtain an identification result of the non-labeled image, and inputting the identification result into a sample selection strategy. Whether the result is reserved or not is judged according to a sample selection strategy, if yes, a pseudo label is generated and moved to the current labeled data set, a new labeled data set continues to be trained, and iterative learning is conducted according to the process till the accuracy is not improved any more. According to the method, the influence of long-tail distribution can be