CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms

Abstract

This study focuses on the detection of Jaboticaba trees in an orchard located in Nanxiong City, Guangdong Province, utilizing UAV platforms to enhance precision agriculture practices. The primary objective is to compress the parameters of deep learning models while improving accuracy to enable their deployment on UAV platforms for rapid Jaboticaba tree identification. The proposed CRE-YOLO model integrates a Cross-Scale Feature Fusion Module (CCFM), the RepDWBlock, and an Efficient Channel Attention (ECA) mechanism, reducing model parameters by 54%, effectively decreasing model complexity while improving detection precision. CRE-YOLO achieves a mean average precision (mAP) of 97.1% at IoU 0.5 and 60.3% at IoU 0.95, with a processing speed of 387 frames per second. Field experiments conducted in the research area identified over 13,000 Jaboticaba trees, demonstrating the model's potential for practical UAV-based orchard management. The study contributes to the modernization of agricultural practices by providing an efficient, scalable solution for rapid and accurate tree detection. Future work will extend the model's application to other crops and focus on enhancing its generalization capabilities.