BanglaVeg:来自孟加拉国的精准农业的精选蔬菜图像数据集
Md Jobayer Ahmed1, Ratu Saha1, Arpon Kishore Dutta1
1Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Data in brief
|March 31, 2025
概括
来自孟加拉国的4319张蔬菜图像的新数据集有助于人工智能开发. 本资源支持使用卷积神经网络 (CNN) 模型进行自动化蔬菜识别和分类.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器学习 机器学习
背景情况:
- 有效的蔬菜识别对于农业和食品供应链至关重要.
- 现有的数据集可能缺乏多样性,特别是对于特定的区域产品.
- 自动化系统需要全面的,现实世界的图像数据进行培训.
研究的目的:
- 为人工智能应用引入孟加拉国蔬菜的新型数据集.
- 为了促进卷积神经网络 (CNN) 模型的开发,用于植物识别.
- 支持农业技术和食品供应链管理方面的进步.
主要方法:
- 收集了12种孟加拉国本土蔬菜物种的4 319张图像.
- 使用手机摄像机在各种自然环境 (市场,田野,家庭) 中捕捉到的图像.
- 通过删除背景和注释关键特征 (形状,纹理,颜色) 预处理图像.
主要成果:
- 开发了蔬菜图像数据集,包括12种不同的蔬菜类型.
- 数据集图像代表了现实世界的条件,增强了模型的概括性.
- 有注释的图像为深度学习提供了丰富的功能信息.
结论:
- 植物图像数据集是人工智能驱动的农业应用的宝贵资源.
- 能够自动识别和分类蔬菜,提高效率.
- 通过采用人工智能,促进贫困社区的技术发展.
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