In agriculture, early detection and localization of plant diseases in time using deep learning techniques can help farmers contain the spread of plant diseases. In this work, we apply object detection models to identify and localize various categories of cassava plant leaf diseases. These include You Only Look Once (YOLO) as well as Generalized Efficient Layer Aggregation Network(GELAN) models. We applied YOLO v9-e, YOLO v9-c, as well as GELAN-e and GELAN-c models. The models were successfully trained using a custom cassava dataset. Several evaluation indicators that include precision, recall and mean average precision(mAP) were analysed. The results have been compared with an earlier version of YOLO model and show an improvement in evaluation indicators reaching above 80% in the majority of diseases.

中文翻译：

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使用对象检测进行木薯作物病害预测和定位


在农业中，使用深度学习技术及早发现和及时定位植物病害可以帮助农民遏制植物病害的传播。在这项工作中，我们应用对象检测模型来识别和定位各种类别的木薯植物叶病。这些模型包括 You Only Look Once （YOLO） 以及广义高效层聚合网络 （GELAN） 模型。我们应用了 YOLO v9-e 、 YOLO v9-c 以及 GELAN-e 和 GELAN-c 模型。这些模型使用自定义木薯数据集成功训练。分析了包括精确率、召回率和平均精确率均值 （mAP） 在内的几个评价指标。结果与早期版本的 YOLO 模型进行了比较，显示大多数疾病的评价指标提高了 80% 以上。