为农业人工智能应用捕获的10个果叶类的综合数据
Minhajul Abedin1, Sujon Islam1, Naznin Sultana1
1Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Data in brief
|July 29, 2025
概括
使用智能手机创建了来自孟加拉十种常见物种的3173张高质量的果叶图像的新数据集. 该资源支持在南亚开发农业计算机视觉应用程序.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 南亚面临着高质量,区域特定的农业图像数据集的稀缺.
- 现有的数据集可能缺乏先进机器学习应用所需的分辨率和标准化.
研究的目的:
- 创建一个全面的,高分辨率的数据集,来自孟加拉国的果树叶.
- 支持该地区农业应用计算机视觉模型的开发.
主要方法:
- 通过使用Realme 7-Pro和8-Pro智能手机收集了十种水果种的3173张叶子图像.
- 在白色背景下的受控照明下拍摄的叶子,捕捉了上方和下方的视图.
- 确保一个平衡的类分布 (每种300-343图像) 机器学习适合性.
主要成果:
- 一个公开可用的数据集,包含标准化,高分辨率的果叶图像.
- 数据集包括各种各样的视觉特性,如叶子形状,花纹和表面纹理.
- 平衡的班级分布适合训练机器学习模型.
结论:
- 该数据集满足了对南亚农业图像数据的需求.
- 促进自动化作物识别和监测系统的开发.
- 能够推动农业计算机视觉研究和应用的进步.
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