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 reduced, the head
category recognition effect is not influenced while the
recall rate and the accuracy rate of the
tail category are improved through iterative learning, reasoning is carried out only by adopting a
single model, an additional
network layer is not introduced, and the reasoning speed is not influenced.