用于分类和细分的病数据集:数据集收集,分析和应用
1Department of Computer Science and Engineering, School of Computing, SRM Institute of Science and Technology, Kattankulathur campus, Chennai 603203, India.
Data in brief
|July 21, 2025
概括
这项研究引入了2500个印度叶图像的新数据集,用于早期发现疾病. 该资源有助于开发智能农业和精准农业的深度学习模型.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 早期诊断叶病对作物健康和农业生产率至关重要.
- 使用深度学习自动检测疾病需要全面的数据集.
- 现有的数据集可能无法捕捉印度农业领域常见的现实世界的变化.
研究的目的:
- 为深度学习模型开发引入印度叶图像的全新,全面的数据集.
- 为植物疾病早期分类,检测和细分提供一个基准资源.
- 支持智能农业,基于机器学习的疾病诊断和精准农业的研究.
主要方法:
- 从印度的实时农田收集了2500张叶图像.
- 将图像分为六个类别:健康和五种常见的疾病 (叶子卷曲病毒,Alternaria叶子斑,Cercospora叶子斑,Phyllosticta叶子斑,菌).
- 为了与标准的深度学习模型兼容,将所有图像调整为 224 × 224 像素.
主要成果:
- 一个独特的数据集2500图像代表健康和生病的树叶在现实世界中条件下.
- 数据集包括照明,叶子定位和环境因素的自然变化.
- 这一数据集是第一批在自然环境中捕获的公开可用的印度叶病数据集之一.
结论:
- 该数据集是研究人员,特别是年轻科学家在智能农业领域的宝贵资源.
- 它使得基于机器学习的疾病诊断和精准农业应用的进步成为可能.
- 未来的工作将扩展数据集,以提高模型的概括性和现实世界的适用性.
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