Paddynet:一个有组织的叶数据集,用于智能化肥推系统
Md Moradul Siddique1, Torikul Islam2, Yeasir Arefin Tusher1
1Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore 7408, Bangladesh.
Data in brief
|September 7, 2023
概括
本研究引入了第一个使用叶色图 (LCC) 标准的公共叶数据集 (PaddyNet). 该数据集帮助农民优化化肥的应用,以提高作物产量.
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:
- 叶分析的公开可用的数据集很少.
- 叶子颜色图 (LCC) 是评估大米植物状况的关键工具.
- 准确的叶评估对于有效的农业管理至关重要.
研究的目的:
- 根据叶子颜色图表 (LCC) 引入第一个公开可用的叶数据集.
- 提供标准化数据集,用于开发用于米健康评估的图像识别模型.
- 支持农民做出有关化肥应用的明智决策.
主要方法:
- 从孟加拉国Jashore收集了560张阿曼大米叶的智能手机图像.
- 根据孟加拉国农业研究院 (BARI) 的指导方针,将图像分类为4个不同的LCC级别.
- 经过验证的数据集分类与领域专家进行准确性和标准化.
主要成果:
- 建立了PaddyNet数据集,这是第一个使用LCC对叶的数据集.
- 该数据集包含4个LCC类别中的560张图像,经过了精心验证.
- 该数据集已准备好用于开发用于LCC级别识别的算法.
结论:
- 帕迪网数据集是农业研发的重要资源.
- 这一数据集将促进创建工具,以帮助农民精确管理.
- 通过数据驱动的洞察力实现了改善的米种植实践.
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