瓜果的新型数据集用于分类和分类
Abdul Khalique Maitlo1, Abdul Aziz1, Hassnian Raza1
1Department of Computer Science SZABIST Larkana, Larkana Pakistan.
Data in brief
|August 14, 2023
概括
这项研究引入了一个新的瓜果数据集,以改善自动收获. 该数据集有助于机器学习开发,用于准确的水果分类和分类,提高农业效率.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 传统的水果收获依赖于手工劳动,导致不准确和经济损失由于过早收集.
- 农业部门有很大的需要先进的科学技术,特别是水果分类和分类.
研究的目的:
- 为开发自主收获系统提供一种新的,原生瓜果品种数据集.
- 促进对农业机器学习和深度学习应用的研究,特别是对果实成熟度的评估.
主要方法:
- 开发一个新的图像数据集,展示巴基斯坦当地瓜品种 (本地辛迪语,Thadrami,Riyali).
- 数据集按三个子文件的成熟度级别 (绿色,成熟绿色,成熟) 进行整理.
- 在受控环境中进行的图像采集,产生了2,309张JPG图像.
主要成果:
- 一个包括2,309张瓜水果图像的综合数据集,跨越不同的成熟阶段.
- 数据集的结构是为了支持机器学习和深度学习模型的培训和验证.
结论:
- 拟议的数据集是推动农业领域机器学习应用的宝贵资源.
- 这项工作有助于开发智能系统,以便更准确,更有效地收获水果.
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