CoSEV: A cotton disease dataset for detection and classification of severity stages and multiple disease occurrence

In the field of agriculture, the advancement of early treatment methods for plant leaf diseases can be greatly improved by utilizing precise and rapid automatic detection techniques. However, two common challenges arise in real-world scenarios: first, identifying different severity stages of diseases, and second, detecting multiple pathogens simultaneously affecting a single plant leaf. Unfortunately, a major hurdle in this research area is the scarcity of publicly available datasets containing images captured under varying conditions. To tackle this issue, we introduce a dataset called CoSEV for cotton diseased leaf images. The CoSEV dataset consists of 496 filtered images of cotton leaves, captured both in controlled conditions and real-field settings using a smartphone camera. After applying augmentation techniques, the total number of images reaches 1186, encompassing a diverse range of situations, including the presence of multiple stresses co-occurring on a single leaf and the progression of disease severity. The dataset has been meticulously organized into 5 classes, with 7 categories representing different levels of cotton curl severity and coexisting diseases. The dataset is uploaded both for detection and classification. To assess the effectiveness of the CoSEV dataset, we conducted experiments using various state-of-the-art detection models. These models were thoroughly analyzed to evaluate their performance in accurately identifying and classifying the different diseases and severity stages present in the dataset.

搜集汇总

背景与挑战

背景概述

CoSEV是一个棉花疾病数据集，专为检测和分类疾病严重阶段及多病共发而设计，包含1186张增强后的图像，涵盖控制条件和真实田间拍摄的棉花叶片，组织成5个类别以表示不同严重程度和共存疾病。该数据集旨在解决农业中疾病识别和分类的实际挑战，并已通过先进检测模型验证其有效性。