Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.

中文翻译：

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YOLOv8s-CGF：小麦穗枯萎病检测的轻量级模型

赤霉病（FHB）是一种影响小麦生产的破坏性病害。准确、快速检测FHB对于提高小麦产量至关重要。传统模型由于参数大、计算量大、资源要求高，难以应用于移动设备。因此，本文提出一种基于改进的YOLOv8s的轻量级检测方法，以方便模型在移动端的快速部署，提高小麦FHB的检测效率。该方法引入了C-FasterNet模块，取代了主干网络中的C2f模块。它有助于减少模型的参数数量和计算量。此外，主干网络中的Conv替换为GhostConv，进一步减少了参数和计算量，且不会显着影响检测精度。第三，Focal CIoU损失函数的引入减少了样本不平衡对检测结果的影响，加速了模型收敛。最后，为了轻量化，从模型中移除了大型目标检测头。实验结果表明，改进模型（YOLOv8s-CGF）的大小仅为11.7 M，占原始模型（YOLOv8s）的52.0%。参数数量仅为5.7×106 M，相当于原模型的51.4%。计算量仅为21.1 GFLOPs，相当于原始模型的74.3%。而且，模型的平均精度（mAP@0.5）为99.492%，比原始模型提高了0.003%，mAP@0.5:0.95比原始模型提高了0.269%。与其他YOLO模型相比，改进后的轻量级模型不仅实现了最高的检测精度，而且显着减少了参数数量和模型尺寸。这为麦穗中的FHB检测以及现场环境下移动终端的部署提供了有价值的参考。