深度神经网络是棉花病害智能识别的一种重要方法。覆盖更多病害、土壤和环境信息的科学数据既是此类方法发展的基础，也是当前的关键制约因素之一。本文提出的棉花病害数据采集自中国海南省三亚市坡田洋高标准农田示范基地中的棉花种植田块，覆盖了炭疽病、细菌性角斑病、褐斑病和枯萎病四种常见棉花病害，包括3453张高分辨率的健康叶片和不同生长阶段的病叶图像。所有样品获取均采用田间随机采样方式，经筛选后由10名棉花病理学专家进行鉴定与标注，同时另选20名标注者对标注后图像进行随机重复标注以检测质量，Vision Transformer模型被引入以进一步验证数据集的稳定性。相对于其他同类数据集，当前数据集数据采集于复杂的田间环境，覆盖了常见棉花病害且具有高分辨率，可更好地服务于棉花病害智能识别模型、算法的研究、训练与验证。

In the realm of cotton disease identification, the Deep Neural Network emerges as a pivotal paradigm. Progress in this sphere hinges on the availability of a comprehensive repository of scientific data, encapsulating a broader spectrum of diseases, variegated soil profiles, and multifaceted environmental attributes. Currently, this dearth of data serves as the principal bottleneck, impeding the advancement of state-of-the-art models and algorithms.Within this scholarly exposition, we present a meticulously curated cotton disease dataset, poised to bridge this knowledge chasm. This dataset comprehensively encompasses four prevalent cotton diseases: anthracnose, bacterial blight, brown spot, and wilt disease. These maladies' exemplars were meticulously gleaned from cotton fields situated in the Potianyang High-standard Farmland Demonstration Base, nestled serenely in Sanya, Hainan Province, China.The dataset unfolds as a magnum opus, comprising 3 453 high-resolution images. These vivid snapshots provide a panoramic view, capturing the pristine vitality of healthy leaves, juxtaposed with leaves beset by disease at various growth stages. The data acquisition, executed with precision, leveraged field random sampling methodologies, ensuring a faithful reflection of the natural complexity in real-world cotton plantations.Every image underwent meticulous scrutiny, with ten seasoned mavens in cotton pathology meticulously overseeing the annotation. An additional cohort of twenty annotators conducted a second round of annotations on randomly selected image subsets, fortifying the dataset's integrity and precision. The Vision Transformer model was employed to guarantee the dataset's resilience and accuracy.This hallowed dataset was meticulously gathered amidst the complexity of field environments, encapsulating the nuances of major cotton diseases in their native habitat. Its high image resolution, akin to an opulent tapestry of visual data, renders it an invaluable resource for pioneering research, astute training, and the relentless validation of astute, intelligent cotton disease recognition models and algorithms. This opulent repository caters to the discriminating tastes of researchers, practitioners, and sagacious decision-makers, furnishing them with a comprehensive and crystalline understanding of the multifaceted tapestry of cotton diseases and their intricate management.