DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images

Maize tassels are critical phenotypic organs in maize, and their quantity is essential for determining tasseling stages, estimating yield potential, monitoring growth status, and supporting crop breeding programs. However, tassel identification in complex field environments presents significant challenges due to occlusion, variable lighting conditions, multi-scale target complexities, and the asynchronous and irregular growth patterns characteristic of maize tassels. In response to these challenges, this paper presents a DMSF-YOLO model for maize tassel detection. In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. A novel DMSF-P2 network architecture is designed, including a multi-scale fusion module (SSFF-D), a scale-splicing module (TFE), and a small object detection layer (P2), which further enhances the model’s feature fusion capabilities. By integrating a dynamic detection head (Dyhead), superior recognition accuracy for maize tassels across various scales is achieved. Additionally, the Wise-IoU loss function is used to improve localization precision and strengthen the model’s adaptability. Experimental results demonstrate that on our self-built maize tassel detection dataset, the proposed DMSF-YOLO model shows remarkable superiority compared with the baseline YOLOv8n model, with precision (P), recall (R), mAP50, and mAP50:95 increasing by 0.5%, 3.4%, 2.4%, and 3.9%, respectively. This approach enables accurate and reliable maize tassel detection in complex field environments, providing effective technical support for precision field management of maize crops.