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用机器学习检测疾病的综合叶图像数据集

Afif Hasan¹, Tanvir Almas Layes¹, Arafat Sahin Afridi²

¹Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

|September 2, 2025

概括

一个新的数据集包括19,000张叶图像, 有助于开发用于早期疾病检测的AI. 这种资源通过准确识别米植物疾病来支持可持续农业和粮食安全.

科学领域:

农业科学
计算机视觉
植物病理学

背景情况:

大米是全球人口很大一部分的基本食物.
准确及时检测米疾病对于维持作物产量和确保粮食安全至关重要.
现有的数据集可能不能完全反映现实世界条件和疾病变异的多样性.

研究的目的:

引入一个全面的,专家注释的叶图像数据集.
促进机器学习模型的开发,用于自动识别病.
通过早期发现疾病来支持可持续的农业实践.

主要方法:

收集了来自BRRI的2,753张原始叶图像,
应用数据增强技术 (旋转,缩放,亮度,翻转) 来创建16247个增强图像.
通过农业学专家的专家注释来准确地分类七种疾病.

主要成果:

一个包含19,000张图像的数据集 (2,753张原始图像,16247张增强图像) 涵盖了7个疾病类别和健康样本.
使用智能手机摄像机拍摄的图像反映了实际的现实情况.
专家注释为模型培训和验证提供可靠的基础真相.

结论:

这一数据集是训练强大的人工智能模型检测病的宝贵资源.
早期发现疾病可以显著改善作物管理,减少产量损失.
这项工作有助于通过技术进步加强依赖大米的地区的粮食安全.

关键词:

深度学习标识方式图像处理机器学习米植物

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