LIGHTWEIGHT CORN LEAF DISEASE DETECTION MODEL BASED ON YOLOV8N-LSCSBD

To achieve mobile deployment of corn leaf disease detection, this study proposes a lightweight method, YOLOv8n-LSCSBD. The Lightweight Shared Convolutional Separable Batch normalization Detection (LSCSBD) is used to achieve cross-scale feature sharing convolution and independent normalization, thereby reducing computational complexity and preserving detection accuracy. Comparisons of YOLOv8 training strategies show that using YOLOv8n as the initial model, with a learning rate of 1e-2 and an optimizer of SGD, yields the best performance. Comparisons of different detection head schemes show that YOLOv8n-LSCSBD reduces the model size by 20.6% (to 5.0MB) compared to the original YOLOv8n model. When compared to YOLOv10n and YOLOv11n, the model size decreased by 13.8% and 9.1%, respectively. Notably, YOLOv8n-LSCSBD achieves P of 97.6%, R of 95.4%, [email protected] of 97.7%, and [email protected]:0.95 of 87.3%. This method provides an efficient lightweight solution for mobile device deployment.