YOLOv9-GSSA model for efficient soybean seedlings and weeds detection

To monitor soybean seedlings growth in real time, an effective method for accurately identifying seedlings and removing weeds is essential. Challenges include the small size and morphological similarity of seedlings and weeds, complicating conventional detection methods. To tackle these issues, we propose a real-time detection algorithm called YOLOv9-GSSA. The improved Mosaic-Dense algorithm increases object density at the model's input layer, enhancing its ability to capture detailed features. Additionally, the GSSA neck optimization module, combining GSConv and Gated Self-Attention, supports key information extraction and multi-scale feature interaction. The Swin-GSSA prediction head further utilizes spatial positional information, improving small object detection and handling overlapping occlusion. Experimental results show our model achieves a mAP of 47.5% with a detection speed of 23.42 ms per image, suitable for real-time monitoring. The enhanced model significantly improves the detection of soybean seedlings and weeds, making it a valuable tool for managing farmland effectively. This ultimately aids in precise yield estimation and decision-making in precision agriculture.