利用智能手机的RGB图像和LiDAR点云进行增强的叶子和树苗生物质评估 - - 作为案例研究的中国菜
Aravind Harikumar1,2, Itamar Shenhar1,2,3, Miguel R Pebes-Trujillo1,2
1School of Materials Science and Engineering, Nanyang Technological University, Singapore, Singapore.
Frontiers in plant science
|August 29, 2025
概括
智能手机与激光雷达相结合, 准确地估计了叶子和叶菜中的生物质. 这种具有成本效益的方法通过优化营养管理和作物产量来支持精准农业.
科学领域:
- 农业科学
- 遥感技术
- 植物生理学
背景情况:
- 准确的植物生理数据对于精准农业至关重要,但通常需要昂贵的设备.
- 基于智能手机的传感为数据收集提供了成本效益较高的替代方案.
- 叶子度和树苗生物量是农作物管理的关键指标.
研究的目的:
- 使用智能手机RGB图像和LiDAR数据开发和验证估计叶子度和拍摄生物质的方法.
- 使用智能手机衍生指数研究植物特征对不同水平的反应.
- 确定最佳的剂量,以最大限度地提高叶菜的产量和活力.
主要方法:
- 整合智能手机的RGB图像和光检测和测距 (LiDAR) 数据.
- 来自RGB图像的光谱特征和来自LiDAR数据的结构特征.
- 应用回归模型 (支持向量,随机森林,拉索) 进行参数估计.
- 使用Amaranthus dubius (中国菜) 的案例研究.
主要成果:
- 精确估计叶子总缩度 (最佳情况RMSE:0.06).
- 精确估计叶子酸盐度 (最佳情况RMSE:0.15).
- 精确估计干燥重量生物质 (最佳情况RMSE:0.05).
结论:
- 通过结合智能手机的RGB图像和LiDAR数据,可以精确估计作物的关键参数.
- 这种方法支持可访问的精准农业实践,以优化营养和产量管理.
- 这种方法为基于智能手机的作物监测和分析提供了基础.
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