Optimization of comprehensive wheat growth index system and monitoring model based on LAI.

Abstract

Wheat growth monitoring plays a vital role in agricultural decision-making and food security. This study aims to develop an accurate and efficient monitoring method for wheat growth by integrating satellite remote sensing and machine learning techniques. Based on preprocessed Sentinel-2 satellite images and measured wheat leaf area index (LAI) data, a set of 11 vegetation indices-such as NDVI, NDRE, and RVI-were selected and ranked through Pearson correlation analysis. A comprehensive index system was then constructed by selecting the top eight indices using a stepwise optimization approach. Three machine learning models-Linear Regression (LR), Backpropagation Neural Network (BPNN), and XGBoost-were applied to evaluate the performance of the index system, with the Particle Swarm Optimization (PSO) algorithm employed to optimize each model. The results demonstrate that the PSO-optimized XGBoost model achieved the highest accuracy (R² = 0.94, MSE = 0.075), exhibiting strong stability and robustness to data fluctuations. These findings suggest that the proposed approach provides a reliable solution for wheat growth monitoring.

Plant phenotyping relevance

衛星リモートセンシングと機械学習により小麦のLAIを推定する監視手法の開発・評価が研究の中心であり、植物キャノピー形質の取得方法を扱っている。

Code and data availability

The paper describes Sentinel-2 imagery from the Copernicus Data Space Ecosystem and a cited prior LAI ground-validation dataset, but provides no authors' deposited phenotype dataset, images, code, or trained models. The Copernicus portal and SNAP/Python/draw.io links are generic public resources, not paper-specific de.

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