甘叶数据集:用于机器学习应用的疾病检测和分类的数据集.
Sandip Thite1, Yogesh Suryawanshi1, Kailas Patil1
1Vishwakarma University, Pune, India.
Data in brief
|March 27, 2024
概括
一个新的甘叶数据集有助于识别九种疾病和叶状况. 该资源支持机器学习,以改善作物管理和提高产量.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 计算机科学 计算机科学
背景情况:
- 甘在全球范围内至关重要,但疾病威胁到产量和质量.
- 准确的疾病检测对于有效的甘管理至关重要.
- 现有的自动疾病识别资源有限.
研究的目的:
- 介绍"甘叶数据集"用于研究.
- 促进机器学习模型的开发,以检测甘疾病.
- 促进疾病自动识别和作物管理方面的进步.
主要方法:
- 编制了6748张高分辨率甘叶图像的数据集.
- 将图像分为9个特定疾病类别,健康和干燥的叶子.
- 数据集包括污染物,黄叶病和棕色生等疾病的图像.
主要成果:
- 现在可以使用"甘叶数据集".
- 数据集包括6748个标记图像.
- 它涵盖了广泛的甘叶病理.
结论:
- 该数据集是农业机器学习应用的宝贵资源.
- 它可以加速自动识别甘疾病的研究.
- 开放获取促进了改善作物产量和管理的合作.
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