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

Abstract

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.

Description

technical field [0001] The invention relates to the field of identification of pests and diseases, in particular to an image identification method for solving the problem of unbalanced samples of pests and diseases of crops. Background technique [0002] Crop pests and diseases are one of the major agricultural disasters in the world. If the pests and diseases are not detected and prevented in time, they may cause heavy losses to agricultural production and threaten the national food security and the quality and safety of agricultural products. Crop diseases and insect pests are characterized by a variety of species, great influence, and frequent outbreaks, which bring great challenges to the monitoring of crop diseases and insect pests. [0003] With the rapid development of computer vision and artificial intelligence, image-based pest and disease identification technology has been applied to the monitoring of various crop pests and diseases with the characteristics of low ...

AI Technical Summary

Problems solved by technology

[0005] In view of the deficiencies in the background technology, the present invention provides an image recognition method that solves the problem of unbalanced samples of crop diseases and insect pests. The specific technical solutions are as follows:

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