A fine-tuned deep learning model for detecting Japanese beetles in soybeans using unmanned aircraft systems (UAS) and mobile imaging

Abstract

Since the early 1900s, the Japanese beetle (Popillia japonica, Newman) has invaded soybean crops in the U.S. It has emerged as a significant economic threat due to its habit of defoliating plants, often leaving them skeletal and reducing yields. The conventional approach for monitoring Japanese beetles uses visual assessments and sweep counts, which are impractical for larger soybean acreages. Furthermore, frequent manual sampling demands labor and time resources that could otherwise be allocated to enhancing soybean production. To address this challenge, we fine-tuned a deep learning model capable of automatically detecting Japanese beetles using images, thereby improving the monitoring process. The YOLOv8s model can detect Japanese beetles 87.90 % of the time on images collected from mobile devices and unmanned aircraft systems (UAS) at certain distances. The model was deployed in a web application as a prototype platform to understand the capabilities of deep learning in pest monitoring. This web application is a server that autonomously analyzes images captured by mobile devices and UAS to detect and count beetles in the soybean canopy. This study aimed to transform the traditional method of pest monitoring in soybean production by transitioning to a digital monitoring system.