Abstract
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.
Abstract (translated)
植物疾病对农民和整个农业部门构成了重大挑战。然而，早期检测植物病害对于减轻其影响和防止广泛损害至关重要，因为疫情爆发会严重影响作物的产量和质量。随着技术的进步，越来越多的机会出现可以自动化监测和识别植物病害的发生。这项研究提出了一种系统，旨在使用迁移学习方法来识别和监控植物疾病。具体来说，该研究利用了YOLOv7和YOLOv8，这两种是目标检测领域中的最新模型。通过在包含植物叶片图像的数据集上微调这些模型，该系统能够准确地检测细菌、真菌和病毒性疾病的出现，如白粉病、角斑病、早疫病以及番茄花叶病毒。模型的性能使用多个指标进行评估，包括平均精度（mAP）、F1分数、精确度和召回率，分别得到了91.05、89.40、91.22和87.66的成绩。 结果表明，YOLOv8在与其他目标检测方法相比时表现出优越的有效性和效率，突显了其在现代农业实践中的应用潜力。该方法为早期任何植物疾病的自动检测提供了一个可扩展的解决方案，有助于提高作物产量、减少对人工监控的依赖，并支持可持续的农业生产方式。
URL
https://arxiv.org/abs/2507.00070
