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咖啡和果仁数据集:用于机器学习应用的检测,分类和产量估计的数据集
Rahman Sanya1, Ann Lisa Nabiryo2, Jeremy Francis Tusubira2
1Department of Adult and Community Education, Makerere University, Kampala, Uganda.
Data in brief
|January 16, 2024
概括
这项研究为乌干达的咖啡和果仁作物提供了有价值的图像数据集,对于开发自动化作物产量估计方法至关重要. 这些数据集将有助于机器学习在农业的进步,特别是在撒哈拉以南非洲.
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:
- 传统的作物产量估计方法昂贵,低效和不准确.
- 准确的作物产量预测对于农场管理和市场规划至关重要.
- 对于基于机器学习的作物产量估计存在很大的数据差距,特别是在撒哈拉以南非洲.
研究的目的:
- 解决农业机器学习专用数据集的缺乏问题.
- 提供咖啡和果仁作物的精选图像数据集.
- 促进自动化作物产量估计技术的发展.
主要方法:
- 在9个月内使用无人机 (UAV) 收集了高分辨率的空中图像.
- 在乌干达两个作物收获季节获得的数据集.
- 有关作物健康和产量相关的对象类 (例如,水果成熟度,树木健康) 的注释图像.
主要成果:
- 精选的数据集包括3000张咖啡和3086张果仁图像 (共6086张).
- 咖啡数据集包括未成熟,成熟,成熟,腐烂和咖啡树的注释.
- 果仁数据集包括树,花,早产,未成熟,成熟和坏坏的注释.
结论:
- 提出的数据集是农业机器学习应用的宝贵资源.
- 这些数据集可以支持诸如产量估计,疾病诊断和成熟度分析等任务.
- 乌干达的数据收集特别针对资源不足的撒哈拉以南非洲地区.
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