Lightweight Field Insect Recognition and Classification Model Based on Improved Deep Learning under Complex Background

Abstract

Insect monitoring in the field is an extremely important part of the agricultural production system. Recent advances in computer technology have provided the technical foundation for automatic field insect monitoring. In insect automatic monitoring, insect recognition and classification based on images is one of the most active research areas. Rapid advancements in computer vision technology based on deep learning have provided new ideas for implementing automatic field insect monitoring. Firstly, the field insect images are preprocessed and input to the lightweight algorithm for feature extraction, and the prediction networks of different sizes are output by multiscale feature fusion; then, the joint cross-merge ratio is introduced for automatic identification and classification of field insects. Compared with other algorithms, the simulation results show that the proposed algorithm has higher accuracy, less time consumption, and stronger robustness. It effectively solves the insect accumulation and background interference problems and can identify field insects online in real time.