Winter Oilseed Rape LAI Inversion via Multi-Source UAV Fusion: A Three-Dimensional Texture and Machine Learning Approach.

Abstract

Leaf area index (LAI) serves as a critical indicator for evaluating crop growth and guiding field management practices. While spectral information (vegetation indices and texture features) extracted from multispectral sensors mounted on unmanned aerial vehicles (UAVs) holds promise for LAI estimation, the limitations of single-texture features necessitate further exploration. Therefore, this study conducted field experiments over two consecutive years (2021-2022) to collect winter oilseed rape LAI ground truth data and corresponding UAV multispectral imagery. Vegetation indices were constructed, and canopy texture features were extracted. Subsequently, a correlation matrix method was employed to establish novel randomized combinations of three-dimensional texture indices. By analyzing the correlations between these parameters and winter oilseed rape LAI, variables with significant correlations ( p p p 2 ) of 0.882, a root mean square error (RMSE) of 0.204 cm 2 cm -2 , and a mean relative error (MRE) of 6.498%. This study provides an effective methodology for UAV-based multispectral monitoring of winter oilseed rape LAI and offers scientific and technical support for precision agriculture management practices.

Plant phenotyping relevance

UAVマルチスペクトル画像とテクスチャ特徴量、機械学習を用いて作物のLAIを推定する手法を開発・評価しており、植物形質取得が研究の中心である。

Code and data availability

The paper describes UAV multispectral imagery, LAI ground-truth measurements, and XGBoost/SVM/PLSR models for winter oilseed rape LAI inversion, but no public phenotype dataset, imagery, code, or trained model is deposited. The Data Availability Statement only offers further inquiries to the corresponding authors, so a

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