Sugarcane-YOLO: An Improved YOLOv8 Model for Accurate Identification of Sugarcane Seed Sprouts

Abstract

Sugarcane is a crop that propagates through seed sprouts on nodes. Accurate identification of sugarcane seed sprouts is crucial for sugarcane planting and the development of intelligent sprout-cutting equipment. This paper proposes a sugarcane seed sprout recognition method based on the YOLOv8s model by adding the simple attention mechanism (SimAM) module to the neck network of the YOLOv8s model and adding the spatial-depth convolution (SPD-Conv) to the tail convolution part. Meanwhile, the E-IoU loss function is chosen to increase the model’s regression speed. Additionally, a small-object detection layer, P2, is incorporated into the feature pyramid network (FPN), and the large-object detection layer, P5, is eliminated to further improve the model’s recognition accuracy and speed. Then, the improvement of each part is tested and analyzed, and the effectiveness of the improved modules is verified. Finally, the Sugarcane-YOLO model is obtained. On the sugarcane seed and sprout dataset, the Sugarcane-YOLO model performed better and was more balanced in accuracy and detection speed than other mainstream models, and it was the most suitable model for seed and sprout recognition by automatic sugarcane-cutting equipment. Experimental results showed that the Sugarcane-YOLO achieved a mAP50 value of 99.05%, a mAP72 value of 81.3%, a mAP50-95 value of 71.61%, a precision of 97.42%, and a recall rate of 98.63%.

Keywords

• sugarcane sprouts
• Sugarcane-YOLO
• SimAM
• SPD-Conv
• E-IoU
• small-object layer