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堆叠机器学习和体积模型的性能,以改善玉米的地表生物质预测

Fu Xuan¹, Wei Su¹, Zhen Chen²

¹College of Land Science and Technology, China Agricultural University, Beijing, 100083, China.

Plant phenomics (Washington, D.C.)

|December 19, 2025

概括

准确的作物生物质预测对农业至关重要. 这项研究开发了一种机器学习模型,使用无人机 (UAV) 数据在各种条件下精确估计玉米地面生物质 (AGB).

科学领域:

农业科学农业科学
遥感遥感遥感遥感
数据科学数据科学数据科学

背景情况:

准确的地面生物量 (AGB) 估计对于作物管理至关重要.
无人驾驶飞行器 (UAV) 和机器学习 (ML) 提供先进的AGB预测方法.
关于这些方法在各种农业条件下的表现的研究有限.

研究的目的:

开发和评估特定的方法来估计玉米AGB在不同的施肥和灌处理下.
为了比较堆叠组合ML模型与植被指数加权天花板体积模型 (CVM_VI) 的性能.
为优化不同作物生长阶段的AGB预测策略提供见解.

主要方法:

利用无人机的LiDAR,多光谱 (MS) 和热红外 (TIR) 数据.
收集了各种增长阶段的AGB和叶面积指数 (LAI) 数据.
开发了一个堆叠集团学习模型,集成多源数据用于AGB预测.

主要成果:

堆叠组合ML模型实现了高的预测准确性 (R² = 0.86,MAE = 1.54 t/ha,RMSE = 2.06 t/ha).
CVM_VI在早期玉米AGB预测中表现出有效性,但随着生物质的增加,准确性下降.
通过数据融合增强的ML模型在中后期AGB预测中表现出优势.

结论:

建议使用数据融合的ML方法来预测中后期的玉米AGB.
CVM_VI适用于早期的AGB估计,提供计算效率.
这项研究提高了AGB预测的准确性和速度,有助于农业决策.

关键词:

在 AGB 预测预测.多源无人机数据多源数据这就是 SHAP SHAP 的意思.堆叠集体学习堆叠集体学习音量模型音量模型音量模型

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