基于轻量化YOLO v8n的自然环境下车前草病虫害检测方法

Detection Method for Pests and Diseases of Plantago asiatica L. in Natural Environments Based on Lightweight YOLO v8n

摘要:

为实现在自然环境下对车前草病虫害的快速准确检测，构建车前草虫害、车前草白粉病、车前草叶斑病和车前草花叶病4种常见病虫害的2290幅图像数据集，并提出一种基于YOLO v8n-Plantago的车前草病虫害检测方法。利用轻量化模型MobileNetV3替换原模型的Backbone，实现网络保持轻量化的同时提高模型响应速度；在模型的Neck部分使用VoV-GSCSP模块，替换第15、18、21层的C2f模块，以更高效地处理特征图并保留更多特征信息；在Head中引入RepVGG模块，加快模型的推理速度、减少模型内存占用量，进一步提高模型的准确率，实现对车前草病虫害快速、准确识别。在自建的车前草病虫害数据集上进行验证，结果显示，YOLO v8n-Plantago在车前草病虫害检测中的mAP@0.5达89.43%，相比于YOLO v8n模型提升2.04个百分点，并且浮点运算量下降61.43%，模型内存占用量下降3.53%，参数量下降7.31%。将模型部署到边缘端设备上，推理速度提升33.26%，后处理速度提升8.08%。改进的YOLO v8n-Plantago模型有效地实现了车前草病虫害检测，为研制车前草病虫害自动识别与精准喷药装置奠定了基础。

Abstract:

Aiming to achieve fast and accurate identification of pests and diseases of Plantago asiatica L. in natural environments, a dataset comprising 2290 images of four common diseases—insect pest, powdery mildew, leaf spot, and mosaic disease—was constructed, and a recognition method for pests and diseases of Plantago asiatica L. based on YOLO v8n-Plantago was proposed. The lightweight MobileNetV3 was utilized to replace the original model’s Backbone, ensuring the network remained lightweight while enhancing response speed. The VoV-GSCSP feature network was applied in the Neck part of the model to replace the C2f modules in layers 15, 18 and 21, which can retain more feature information while processing the feature map more efficiently. The RepVGG module was introduced in the Head of the network to accelerate inference speed and reduce memory footprint, further improving model accuracy for rapid and accurate recognition of Plantago asiatica L. pests and diseases. Validation on the self-built dataset of pest and disease of Plantago asiatica L. showed that YOLO v8n-Plantago achieved mAP@0.5 of 89.43% for identifying Plantago asiatica L. pests and diseases, an increase of 2.04 percentage points compared with that of the YOLO v8n model, with FLOPs reduced by 61.43%, model memory usage decreased by 3.53%, and parameter count reduced by 7.31%. When deployed on an edge device, inference speed was increased by 33.26%, and processing speed was increased by 8.08%. The proposed YOLO v8n-Plantago model effectively facilitated the detection of Plantago asiatica L. pests and diseases, laying a foundation for developing automatic identification and precise spraying devices for Plantago asiatica L. pests and diseases.