Research on a machine vision-based electro-killing pheromone-baited intelligent agricultural pest monitoring method

作者：Guozhi Li, Zhengbo Liu, Zelin Feng, Jun Lyu, Bin Li, Chen Guo, Qing Yao · 发表于：Frontiers in Plant Science · 年份：2025 · DOI：10.3389/fpls.2025.1521594 · 被引用次数：4 · 研究领域：Advanced Chemical Sensor Technologies、Smart Agriculture and AI、Food Supply Chain Traceability

The annual global economic losses from pest-induced crop damage are substantial and difficult to quantify. Real-time monitoring of pest dynamics and timely control strategies are crucial for food security. Among the primary monitoring techniques, sex pheromone-baited trapping technology is instrumental in the detection and management of agricultural pests. To address existing limitations-such as manual insect collection and counting in conventional traps, inaccuracies in photoelectric counting devices, and the requirement for manual replacement of sticky boards in image-based traps-an advanced agricultural pest monitoring system utilizing sex pheromone bait was designed and developed in this paper. The system integrates smart electro-killing pheromone traps, a pest detection model, and a pheromone monitoring platform. Male pests attracted to pheromones are neutralized by an electric grid and deposited on an image acquisition platform. A network camera captures images of the pests, which are processed by a YOLOv9-TrapPest detection model to identify and quantify them. This model incorporates an AKConv module to enhance feature extraction, reducing false detections from limb separation. The CBAM-PANet structure improves detection rates of sticky pests, while the FocalNet module optimizes fine-grained feature capture, excluding non-target pests. The YOLOv9-TrapPest model outperforms other detection models, achieving 97.5% average precision and 98.3% mAP50 for detecting seven pes...