Maize Leaf Area Index Estimation Based on Machine Learning Algorithm and Computer Vision

Wanna Fu, Zhen Chen, Qian Cheng, Yafeng Li, Weiguang Zhai, Fan Ding, Xiaohui Kuang, Deshan Chen and Fuyi Duan ()
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Wanna Fu: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Zhen Chen: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Qian Cheng: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Yafeng Li: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Weiguang Zhai: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Fan Ding: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Xiaohui Kuang: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Deshan Chen: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China
Fuyi Duan: Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China

Agriculture, 2025, vol. 15, issue 12, 1-22

Abstract: Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R 2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R 2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R 2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making.

Keywords: unmanned aerial vehicle; vegetation indices; machine learning; computer vision; leaf area index (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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