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一个新的花生叶数据集,用于检测和分类花生叶疾病
Buddhadev Sasmal1,2, Arunita Das2, Krishna Gopal Dhal2
1Department of Pure and Applied Science, Midnapore City College, Paschim Medinipur, West Bengal, India.
Data in brief
|August 19, 2024
概括
一个新的数据集,包括1720张花生叶片图像,有助于早期发现疾病. 该资源支持开发深度学习模型,以帮助农民识别疾病并提高作物产量.
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:
- 地瓜 (Arachis hypogaea) 对于全球粮食安全至关重要,提供油和蛋白质.
- 地瓜生产受到诸如叶子斑,生和罗塞特等疾病的威胁.
- 深度学习 (DL) 在植物疾病诊断方面表现有前途,但地瓜特定数据集很少.
研究的目的:
- 为DL模型培训创建一个全面的花生叶图像数据集.
- 促进自动化地瓜疾病识别工具的开发.
- 支持用于植物健康管理的DL应用的研究.
主要方法:
- 收集了1720张健康和生病的花生叶子的原始图像,使用智能手机摄像机在现实现场条件下拍摄.
- 该数据集是在西孟加拉邦的Ramchandrapur,Purba Medinipur捕获的.
- 应用最先进的卷积神经网络 (CNN) 架构用于基线疾病分类.
主要成果:
- 数据集包括1720张适用于训练DL模型的图像,用于早期检测地病.
- 基线CNN结果证明了数据集对准确地分类花生病的潜力.
- 该研究为未来在地种植中DL应用提供了基础结果.
结论:
- 开发的数据集解决了在DL研究中对地特异性数据的关键需求.
- 这种资源可以显著推动农民开发工具,以提高地产量.
- 通过先进的计算方法,通过先进的计算方法,促进早期和准确的地植物疾病的识别.
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