YOLO_SSP: An Auto-Algorithm to Detect Mature Soybean Stem Nodes Based on Keypoint Detection

The soybean stem node is a key part of soybean growth and development, and its numbers play a crucial role in soybean yield formation. Traditional manual methods are labor-intensive and error-prone. The keypoint detection method is an ideal choice for stem node detection due to its high accuracy and wide applicability. In this study, a new deep learning method, You Only Look Once _Soybean Stalk Pose (YOLO_SSP) was proposed, which innovatively applied the Small_Effective Low-Level Aggregation Network (S_ELAN) module and fused it with a smaller detection head for detecting stem nodes in mature soybeans. After optimization and iteration, the model achieved 88.1% accuracy on the dataset. Subsequently, by ablating the model, it was found that different improvements were effective in increasing the accuracy of the model. In addition, when comparing the classic YOLO series of keypoint detection models, the results show that YOLO_SSP achieved up to 87.7% of APs, which was higher than YOLOv7-w6-pose, YOLOv7-tiny-pose, YOLOv3s-pose, YOLOv5n-pose, YOLOv5s-pose, YOLOv5m- pose, YOLOv6n-pose, YOLOv8n-pose, and YOLOv10b-pose, which were 2.5%, 12.8%, 5.3%, 3.8%, 3.5%, 3.5%, 5.1%, 5.1%, 5.0%, and 4.5% higher, respectively. Finally, the proposed model was applied to the unique dataset with 85.3% precision and 82.6% accuracy, and the visualization of the model’s detection results proved its applicability and universality. This study provides an effective strategy for soybean stem node detection and significantly improves the accuracy of detection.