ADQ-YOLOv8m: a precise detection model of sugarcane disease in complex environment

Current research on sugarcane disease identification primarily focuses on a limited number of typical diseases, often constrained by specific target groups or conditions. To address this, we propose an enhanced ADQ-YOLOv8m model based on the YOLOv8m framework, enabling precise detection of sugarcane diseases.

The detection head is modified to a Dynamic Head to enhance feature representation capabilities. Following the Detect module, we introduce the ATSS dynamic label assignment strategy and the QFocalLoss loss function to address issues such as class imbalance, thereby bolstering the model's feature representation capabilities.

Experimental results demonstrate that ADQ-YOLOv8m outperforms nine other mainstream object detection models, achieving precision, recall, mAP50, mAP50-95, and F1 scores of 86.90%, 85.40%, 90.00%, 77.40%, and 86.00%, respectively.

Finally, comprehensive evaluation of the ADQ-YOLOv8m model's performance is conducted using visual analysis of image predictions and cross-scenario adaptability testing. The experimental results indicate that the proposed model excels in multi-objective processing and demonstrates strong generalization capabilities, suitable for scenarios involving multiple objectives, multiple categories, and class imbalance. The detection method proposed exhibits excellent detection performance and potential, providing robust support for the development of intelligent sugarcane cultivation and disease control.