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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月21日

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  • Mingguang Diao,
  • Shuning Liang,
  • Jianing Chen,
  • Chuyan Zhang,
  • Yong Liu

摘要

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.