A method for automatic real-time detection and counting of fruit fly pests in orchards by trap bottles via convolutional neural network with attention mechanism added

Abstract

Fruit fly pests seriously affect the quality and safety of various melons, fruits, and vegetable crops. Many farmers lack sufficient knowledge of the level of pest occurrence, which leads to the over-use of pesticides, resulting in environmental pollution and quality degradation of agricultural products. The combination of artificial intelligence and agricultural technology can continuously and dynamically monitor pests in orchards, help scientific researchers and fruit farmers master pest data in time, reduce the use of artificial and pesticides, and achieve scientific early warning and prevention of pests. In this paper, the sexual attractant is placed in the pest trap bottle, the optical flow method, U-Net semantic segmentation, and the YOLOv5 algorithm with SE, CBAM, CA, and ECA attention mechanisms are used to detect and count live Bactrocera cucurbitae on the bottle surface, and then we use Hough circle detection method to detect the entrance position of the trap bottle, and finally count the number of B. cucurbitae entering the trap bottle in combination with the position information of B. cucurbitae and the entrance of the trap bottle. The experimental results show that: the accuracy of counting B. cucurbitae on the surface of the trap bottle can reach 93.5%, and the accuracy of counting B. cucurbitae entering the trap bottle can reach 94.3%, which can dynamically monitor the number of pests in the orchard in real time.