Estimation of Wheat Leaf Water Content Based on UAV Hyper-Spectral Remote Sensing and Machine Learning

Yunlong Wu, Shouqi Yuan, Junjie Zhu, Yue Tang and Lingdi Tang ()
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Yunlong Wu: Research Center of Fluid Machinery Engineering and Technology, Data and Informatization Department, Faculty of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Shouqi Yuan: Research Center of Fluid Machinery Engineering and Technology, Data and Informatization Department, Faculty of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Junjie Zhu: Research Center of Fluid Machinery Engineering and Technology, Data and Informatization Department, Faculty of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Yue Tang: Research Center of Fluid Machinery Engineering and Technology, Data and Informatization Department, Faculty of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Lingdi Tang: Research Center of Fluid Machinery Engineering and Technology, Data and Informatization Department, Faculty of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 17, 1-18

Abstract: Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR ( R 2 = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR ( R 2 = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF ( R 2 = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability.

Keywords: hyperspectral remote sensing; leaf water content; wheat; machine learning; spectral preprocessing (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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