Image Dataset for Disease Detection in Black Gram (Vigna mungo) Leaves: A Resource for Machine Learning Research
收藏资源简介：
This dataset presents a curated collection of images of Black Gram (Vigna mungo) leaves, annotated with labels for healthy leaves and various common diseases. Created to support the advancement of machine learning and computer vision models in agricultural disease detection, this dataset is valuable for researchers and practitioners working in botany, plant pathology, agriculture, and artificial intelligence. The dataset is designed to reflect real-world agricultural conditions, providing a robust foundation for developing disease detection and classification models that can aid in crop health monitoring and management. Dataset Content: The dataset includes a total of 4,038 images representing healthy leaves and five distinct disease categories. Each category offers a range of visual variations, including different background conditions, lighting, and severity of disease symptoms, ensuring comprehensive data diversity. This resource can be used for training, testing, and validating machine learning models for image-based disease classification and detection tasks. The dataset is organized as follows: Healthy: 545 images Cercospora leaf spot: 598 images Leaf Crinkle: 806 images Insect: 408 images Yellow Mosaic: 1,681 images Purpose: The primary aim of this dataset is to facilitate the development of machine learning models that can accurately detect and classify diseases in Black Gram leaves, supporting early diagnosis and promoting effective crop management strategies. This dataset serves as a resource for improving automated plant disease diagnosis, contributing to agricultural sustainability and food security.
本数据集为经筛选整理的黑绿豆（Black Gram, Vigna mungo）叶片图像集合，针对健康叶片与各类常见病害叶片标注了对应标签。本数据集旨在推动农业病害检测领域机器学习与计算机视觉模型的研发进展，可为植物学、植物病理学、农学以及人工智能领域的研究人员与从业者提供重要支撑。本数据集贴合真实农业生产场景，可为开发用于作物健康监测与管理的病害检测及分类模型提供坚实基础。 数据集内容：本数据集共包含4038张图像，涵盖健康叶片以及5个不同的病害类别。每个类别均包含丰富的视觉差异样本，涵盖不同背景环境、光照条件以及病害症状严重程度，确保数据集具备全面的数据多样性。该资源可用于训练、测试与验证面向图像病害分类与检测任务的机器学习模型。数据集具体分类如下： 健康叶片：545张 尾孢叶斑病（Cercospora leaf spot）：598张 叶片皱缩病（Leaf Crinkle）：806张 虫害（Insect）：408张 黄花叶病（Yellow Mosaic）：1681张 数据集用途：本数据集的核心目标是助力开发可精准检测并分类黑绿豆叶片病害的机器学习模型，支持病害早期诊断，推动高效作物管理策略的实施。本数据集可为自动化植物病害诊断技术的优化提供支撑，助力农业可持续发展与粮食安全保障。
