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使用来自无人机多光谱图像和机器学习模型的多源特征估计玉米叶面积指数

Hongyan Li^1,2, Caixia Huang^1,2, Yuze Zhang^1,2

¹College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China.

Plants (Basel, Switzerland)

|November 27, 2025

概括

这项研究开发了一种使用无人机图像来估计玉米叶面积指数 (LAI) 的新方法. 结合各种数据特征显著提高了精准农业作物生长监测的准确性.

科学领域:

农业遥感农业遥感
植物生理学植物生理学
机器学习在农业中的应用

背景情况:

叶面积指数 (LAI) 对于评估作物健康和产量至关重要.
无人机多光谱图像提供了丰富的数据,但在使用单个特征进行LAI估计方面存在局限性.
准确的LAI估计对于精准农业和有效的作物管理至关重要.

研究的目的:

开发和评估一个多源特征融合框架,用无人机多光谱图像来估计玉米的LAI.
整合植被指数 (VIs),纹理特征 (TFs) 和纹理指数 (TI) 以提高LAI估计.
评估堆叠组合机器学习模型 (PLSR,SVM,RF,GBDT) 对于玉米LAI预测的性能.

主要方法:

进行了不同种植密度和率的玉米实验.
获得无人机多光谱图像以提取VI,TF和TI.
采用堆叠组合方法,将PLSR与SVM,RF和GBDT算法相结合,用于特征融合和LAI估计.

主要成果:

综合框架显著提高了LAI估计的准确性,而不是仅仅使用VI.
将VI,TF和TI与PLSR+GBDT的融合实现了最高的R2 (0.844) 和最低的RMSE (0.436).
独立验证证实了多模型融合框架 (PLSR+GBDT) 的稳定性,R2值为0.859和0.794.

结论:

使用机器学习的多源功能集成提高了玉米LAI估计的准确性和稳定性.
开发的框架为精准农业和实时作物生长监测提供了有价值的工具.
这种方法克服了基于遥感的作物评估中单一特征分析的局限性.

关键词:

无人机多光谱图像无人机多光谱图像叶面积指数叶面积指数机器学习是机器学习.玉米玉米玉米是一种质地特征质地特征质地特征植被指数植被指数

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