CoSEV: A cotton disease dataset for detection and classification of severity stages and multiple disease occurrence
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In agriculture, the development of early treatment techniques for plant leaf diseases can be significantly enhanced by employing precise and rapid automatic detection methods. Within this realm of research, two common scenarios encountered in real field cases are the identification of different severity stages of diseases and the detection of multiple pathogens simultaneously affecting a single plant leaf. One major challenge faced in this area is the lack of publicly available datasets that contain images captured under these specific conditions. To address this challenge, we present a dataset called CoSEV in this paper. The CoSEV dataset comprises a collection of 496 images of cotton leaves, captured both under controlled conditions and in real-field settings using a smartphone camera. Thge total number of images after applying augmentation techniques is 1151.It covers a diverse range of situations, including multiple stresses co-occurring on a single leaf and the progression of disease severity. The dataset was carefully organized into 5 classes, with 7 categories representing different levels of cotton curl severity and coexisting diseases. To evaluate the effectiveness of the CoSEV dataset, we trained and tested various state-of-the-art detection models. These models were analyzed to assess their performance in accurately identifying and classifying the various diseases and severity stages present in the dataset.
在农业领域，采用精准快速的自动检测方法，可有效推动植物叶片病害早期防治技术的发展。在该研究领域中，实际田间场景下存在两类典型研究场景：一是识别病害的不同严重程度等级，二是检测同时侵染单张植物叶片的多种病原菌。该领域当前面临的一大挑战是，缺乏包含此类特定场景图像的公开可用数据集。为解决这一难题，本文提出了名为CoSEV的数据集。CoSEV数据集包含496张棉花叶片图像，这些图像分别在可控环境与实际田间场景下通过智能手机摄像头拍摄获取。经数据增强技术处理后，总图像量达1151张。该数据集涵盖多种复杂场景，包括单张叶片上同时出现多种胁迫，以及病害严重程度的递进变化。该数据集经精心划分为5个大类，下设7个类别，分别对应棉花卷叶病的不同严重程度等级以及并发病害。为评估CoSEV数据集的应用效果，本文基于该数据集训练并测试了多款当前主流的先进检测模型。通过对这些模型的分析，评估其在精准识别与分类数据集中各类病害及病害严重程度等级方面的性能表现。
