Deep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture

Abstract

The growing global population and increasing food demand highlight the need for sustainable agricultural practices that balance productivity with environmental protection. Traditional blanket pesticide spraying leads to overuse of chemicals, environmental pollution, and biodiversity loss. This study aims to develop an innovative approach to precision pest management using mobile computing, computer vision, and deep learning techniques. A mobile measurement platform equipped with cameras and an onboard computer was designed to collect real-time field data and detect pest infestations. The system uses an advanced object detection algorithm based on the YOLOv4 architecture, trained on a custom dataset of rapeseed pest images. Modifications were made to enhance detection accuracy, especially for small objects. Field tests demonstrated the system’s ability to identify and count pests, such as the pollen beetle (Brassicogethes aeneus), in rapeseed crops. The collected data, combined with GPS information, generated pest density maps, which can guide site-specific pesticide applications. The results show that the proposed method achieved a mean average precision (mAP) of 83.7% on the test dataset. Field measurements conducted during the traversal of rapeseed fields enabled the creation of density maps illustrating the distribution of pollen beetles. Based on these maps, the potential for pesticide savings was demonstrated, and the migration dynamics of pollen beetle were discussed.

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