Advanced YOLOv8-Based Efficient Detection Method for Sugarcane Stem Nodes

The precise identification of sugarcane stem nodes is crucial for intelligent seed cutting, planting placement, sugarcane garden production management process optimization, yield improvement, and economic benefits. However, efficiency, model complexity, and real-time performance are still issues with the sugarcane stem node detection methods are currently in use. This study decided to visually detect distinguish between sugarcane stem nodes in a structured scene using the advanced YOLOv8 model in order to address this issue. For the purpose of to create an image training set and a test set, a field sugarcane image collection experiment was first designed. The collected sugarcane images were manually labeled. The YOLOv8 network was then utilized as the sugarcane stem node detection model to find the ideal hyperparameter combination and train the model. Finally, field-based recognition experiments are carried out to verify the method's effectiveness and efficiency. Experimental results show that the precision, recall, mAP, single-frame inference time and model size of our method on the test set are 0.973, 0.958, 0.974,19.80 ms and 6.30 MB respectively. Compared with the Edgeyolo_S_Coco network and Edgeyolo_Tiny network, the mAP value of the YOLOv8_n network has increased by 1.70% and 1.30% respectively, the single-frame inference time has been reduced by 4.71 ms and 1.50 ms respectively, and the model size has been reduced by 33.70 MB and 17.50 MB respectively. This method has advantages in detection performance and generalization ability, and can effectively meet the requirements for algorithm accuracy and model complexity in outdoor environments, providing solid technical support for sugarcane harvesting and planting in intelligent agricultural production.