一个脉冲作物的农学特征和来自多个环境的多谱图像数据集
Kingsley Umani1, Chongyuan Zhang1,2, Rebecca J McGee3
1Department of Biological System Engineering, Washington State University, Pullman, WA, United States.
Data in brief
|March 4, 2024
概括
无人机 (UAV) 多光谱图像捕捉了干豆和豆育种试验中的作物产量潜力. 来自无人机数据的数字表型有助于评估植物性能和改善作物品种.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 植物育种 植物育种
背景情况:
- 农作物产量潜力对于育种计划至关重要.
- 无人机 (UAV) 多光谱成像为数据收集提供了先进的方法.
- 数字表型,如植被指数,可以评估作物活力和健康.
研究的目的:
- 为了呈现一个数据集的多光谱图像和农学数据干豆和小豆.
- 为了能够评估不同品种和繁殖系的作物表现.
- 支持在作物育种计划中使用基于无人机的遥感.
主要方法:
- 在多个生长阶段使用无人机与5频段摄像头收集多谱图像.
- 从三个地点和三年 (2017-2019) 的干豆和豆育种试验中获得的农学和现象学数据.
- 在标签图像格式 (TIF) 中生成未校准的合式orthomosaic图像.
主要成果:
- 该数据集包括超过275张多光谱图像和详细的种子产量信息.
- 对品种和繁殖系记录了农学和现象学数据.
- 整形图像可供进一步处理,以获得植被指数和其他特征.
结论:
- 基于无人机的多光谱图像是捕捉作物产量潜力的宝贵工具.
- 提出的数据集有助于评估作物表现,并有助于选择改进品种.
- 该资源支持精准农业和作物育种研究的进步.
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