Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model

Abstract

Maize tassel detection is essential for future agronomic management in maize planting and breeding, with application in yield estimation, growth monitoring, intelligent picking, and disease detection. However, detecting maize tassels in the field poses prominent challenges as they are often obscured by widespread occlusions and differ in size and morphological color at different growth stages. This study proposes the SEYOLOX-tiny Model that more accurately and robustly detects maize tassels in the field. Firstly, the data acquisition method ensures the balance between the image quality and image acquisition efficiency and obtains maize tassel images from different periods to enrich the dataset by unmanned aerial vehicle (UAV). Moreover, the robust detection network extends YOLOX by embedding an attention mechanism to realize the extraction of critical features and suppressing the noise caused by adverse factors (e.g., occlusions and overlaps), which could be more suitable and robust for operation in complex natural environments. Experimental results verify the research hypothesis and show a mean average precision (mAP@0.5) of 95.0%. The mAP@0.5, mAP@0.5–0.95, mAP@0.5–0.95 (area=small), and mAP@0.5–0.95 (area=medium) average values increased by 1.5, 1.8, 5.3, and 1.7%, respectively, compared to the original model. The proposed method can effectively meet the precision and robustness requirements of the vision system in maize tassel detection.