一个全面的图像数据集的病病
Md Masudul Islam1, Md Ripon Sheikh1
1Bangladesh University of Business and Technology, Dhaka, Bangladesh.
Data in brief
|December 24, 2025
概括
一个新的朱特病图像数据集帮助机器学习用于植物疾病诊断. 该资源支持早期检测 (Corchorus) 病,这对农业至关重要.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
背景情况:
- 准确诊断 (Corchorus) 疾病对于作物产量至关重要.
- 标记病图像数据的有限可用性阻碍了机器学习模型的开发.
研究的目的:
- 为机器学习应用提供一个全面的朱特病图像数据集.
- 促进自动化系统的开发,用于鉴定木植物疾病.
主要方法:
- 收集和策划了1390张高分辨率的木质植物叶片图像.
- 将图像分为五个类别:衰老,洞,马赛克,茎软腐烂和新鲜 (健康).
- 应用预处理,包括大小调整 (1024x1024) 和重复删除;创建增强的火车测试分割版本.
主要成果:
- 该数据集包括1390张图像,涵盖五个不同的乳汁病类别.
- 在各种条件下捕获的图像增强了模型的概括性.
- 可视化技术 (Grad-CAM) 验证了对疾病相关区域的模型注意力.
结论:
- 朱特病图像数据集解决了农业AI的关键数据缺口.
- 能够及时管理疾病,并支持果汁种植,特别是在孟加拉国等关键地区.
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