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用于精准农业的葡萄叶病数据集

Madhuri Dharrao¹, Nilima Zade¹, R Kamatchi²

¹Department of Computer Science & Engineering, Symbiosis Institute of Technology Pune, Symbiosis International (Deemed University), Pune 412115, Maharashtra, India.

|July 2, 2025

概括

一个新的数据集,包括2,726张葡萄叶图像,有助于识别菌病,如菌和粉状菌. 这个资源支持人工智能模型的早期检测,提高葡萄的产量和质量.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
机器学习机器学习

背景情况:

在全球范围内,葡萄种植至关重要,但真菌病显著降低了产量和质量.
准确及时识别疾病对于有效的作物管理和经济可行性至关重要.
现有的葡萄疾病检测方法可能缺乏先进的人工智能应用所需的规模和精度.

研究的目的:

引入一个全面的,高质量的葡萄叶图像数据集,用于疾病检测.
促进先进的人工智能模型的开发,以自动识别和分类葡萄叶病.
为计算机视觉和农业技术领域的研究人员提供一个开放的资源.

主要方法:

在印度纳西克两年 (2023-2025) 收集了2,726张高分辨率葡萄叶图像.
专家对图像进行了注释和组织,将其分为"健康"和"不健康"类别,不健康的叶子进一步分为,粉和细菌叶子斑.
使用转移学习方法与ResNet-18算法验证数据集.

主要成果:

数据集包括2,726张葡萄叶的精心注释的图像.
ResNet-18模型实现了96%的分类准确度,证明了数据集的高质量和适合深度学习.
数据集分为健康的叶子和三个主要疾病类别:菌,粉状菌和细菌叶子斑.

结论:

开发的葡萄叶病图像数据集是推动人工智能驱动的农业疾病检测的宝贵资源.
取得的高分类准确性验证了数据集适合训练强大的机器学习模型的适用性.
这一开放式数据集将支持对可持续葡萄生产和改善疾病管理策略的研究和开发.

关键词:

计算机视觉计算机视觉计算机视觉疾病的分类疾病的分类.葡萄疾病数据集葡萄疾病数据集机器学习机器学习精准农业精准农业精准农业

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