Lightweight Network for Corn Leaf Disease Identification Based on Improved YOLO v8s

Abstract

This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating the lightweight GhostNet (Ghost Convolutional Neural Network) structure, which replaces the YOLO v8s backbone. This adaptation involves swapping the head’s C2f (Coarse-to-Fine) and Conv (Convolutional) modules with C3 Ghost and GhostNet, simplifying the model architecture while significantly amplifying detection speed. Additionally, a lightweight attention mechanism, Triplet Attention, is incorporated to refine the accuracy in identifying the post-neck layer output and to precisely define features within disease-affected areas. By introducing the ECIoU_Loss (EfficiCLoss Loss) function, replacing the original CIoU_Loss, the algorithm effectively mitigates issues associated with aspect ratio penalties, resulting in marked improvements in recognition and convergence rates. The experimental outcomes display promising metrics with a precision rate of 87.50%, a recall rate of 87.70%, and an mAP@0.5 of 91.40% all within a compact model size of 11.20 MB. In comparison to YOLO v8s, this approach achieves a 0.3% increase in mean average precision (mAP), reduces the model size by 50.2%, and significantly decreases FLOPs by 43.1%, ensuring swift and accurate maize disease identification while optimizing memory usage. Furthermore, the practical deployment of the trained model on a WeChat developer mini-program underscores its practical utility, enabling real-time disease detection in maize fields to aid in timely agricultural decision-making and disease prevention strategies.