Improving maize LAI estimation by integrating multispectral imagery and digital surface models with ensemble learning.

Abstract

To overcome the limitations of single remote-sensing features in estimating maize canopy leaf area index (LAI), this study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning. Field experiments were conducted in Dehong, Yunnan Province, China, during 2023-2024, and UAV multispectral images and DSM products were acquired for maize grown under three planting-density treatments. Five vegetation indices and three DSM-derived texture/structural features were retained according to their correlation with measured LAI, statistical significance, and complementary spectral or structural information. The VI-based random forest (VI-RF) model achieved an R 2 of 0.835 and an NRMSE of 9.5%, whereas the DSM-based model showed lower performance (R 2 = 0.641; NRMSE = 14.2%). Under the same random-forest modeling framework, fusing VIs with DSM features improved the overall model performance to R 2 = 0.892 and NRMSE = 7.6%, indicating that DSM-derived structural information mainly enhanced the feature representation of maize LAI. Using the same VI-DSM feature set, the stacking model with support vector machine (SVM) as the meta-learner further improved the overall performance to R 2 = 0.930 and NRMSE = 6.3%. The additional gain from stacking was moderate but consistent, whereas feature fusion contributed the dominant improvement. The combined VI-DSM-Stacking workflow improved prediction stability across planting densities, especially under low- and high-density canopy conditions where soil background interference and spectral saturation were more evident. These results demonstrate that integrating spectral and DSM-derived structural information with stacking ensemble learning can improve the accuracy and robustness of UAV-based maize LAI estimation.

Plant phenotyping relevance

UAV画像・DSM・アンサンブル学習を統合し、トウモロコシのLAI推定法を開発・比較検証しており、表現型取得・推定手法が研究の中心である。

Code and data availability

The supplied blocks describe UAV multispectral imagery, DSM products, LAI measurements, and Python/scikit-learn modeling, but contain no data availability statement, no public repository deposit, and no authors' URL for datasets, images, code, or trained models. The only URL present is the article's own DOI, which is a

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