用迁移学习实现高效植物病害检测，准确率超91%。
An efficient plant disease detection using transfer learning approach
- 基于YOLOv7和YOLOv8模型，通过微调植物叶片图像数据集进行检测。
- mAP达91.05%，对细菌、真菌、病毒病均实现高精度识别。
- 适合农业自动化监测，助力智慧农业与可持续发展。
植物病害对农民和农业部门构成重大挑战。早期检测对减轻其影响、防止广泛损害至关重要，因疫情可能严重降低作物产量和品质。随着技术进步，植物病害的自动化监测与检测机会增多。本研究提出一种基于迁移学习的植物病害识别与监测系统，采用YOLOv7和YOLOv8两个先进的目标检测模型。通过在植物叶片图像数据集上微调，系统可准确检测细菌、真菌和病毒类病害，如白粉病、角斑病、早疫病和番茄嵌纹病毒。使用平均精度（mAP）、F1分数、精确率和召回率等指标评估性能，分别获得91.05、89.40、91.22和87.66的得分。结果表明，相比其他检测方法，YOLOv8表现更优，具有显著的有效性与效率，具备在现代农业中应用的潜力。该方法为早期病害检测提供可扩展、自动化的解决方案，有助于提升作物产量，减少人工监测依赖，支持可持续农业实践。
原文摘要 · Abstract (English)
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
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