Predicting leaf area index (LAI) is essential for understanding crop growth status and water-fertilizer management. The aim of this study was to evaluate the effectiveness of unmanned aerial vehicle (UAV) multispectral imaging technology in estimating the LAI of corn under different water and fertilizer stress conditions. Monitoring of corn at different growth stages under three water and four fertilizer treatments was conducted using field-based multispectral imaging. Subsequently, a stacking ensemble learning model with primary learners including deep forest (DF), deep neural network, support vector regression, and linear regression was established to predict LAI. Results indicated a significant impact of water and fertilizer stress on LAI. After multi-stage data fusion, the highest Pearson correlation coefficient between vegetation index acquired by UAV and LAI measured on the ground was 0.794. The ensemble learning algorithm utilizing DF as a secondary learner outperformed individual machine learning algorithms, demonstrating prediction metrics with R² = 0.876, MAE = 351, RMSE = 0.481. The study suggests that ensemble learning algorithms could replace individual machine learning algorithms in constructing LAI prediction models. This research provides theoretical guidance for rapid and precise water and fertilizer management in large experimental fields.

预测叶面积指数 (LAI) 对于了解作物生长状况和水肥管理至关重要。本研究的目的是评估无人机多光谱成像技术在不同水肥胁迫条件下估算玉米 LAI 的有效性。使用现场多光谱成像对三种水和四种肥料处理下不同生长阶段的玉米进行监测。随后，建立了包含深度森林（DF）、深度神经网络、支持向量回归和线性回归等主要学习器的堆叠集成学习模型来预测LAI。结果表明水肥胁迫对 LAI 有显着影响。经过多阶段数据融合，无人机获取的植被指数与地面测量的LAI之间的皮尔逊相关系数最高为0.794。使用 DF 作为辅助学习器的集成学习算法优于单独的机器学习算法，展示了 R ²  = 0.876、MAE = 351、RMSE = 0.481 的预测指标。该研究表明，在构建 LAI 预测模型时，集成学习算法可以取代单独的机器学习算法。该研究为大试验田快速精准水肥管理提供理论指导。