Accurate Inversion of Rice LAI Using UAV-Based Hyperspectral Data: Integrating Days After Transplanting and Meteorological Factors

Nan Wang, Shilong Li, Xin Qi, Meihan Liu, Jiayi Yang, Jiulin Zhou, Lihong Yu, Fenghua Yu, Chunling Chen () and Yonghuan Wang ()
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Nan Wang: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Shilong Li: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xin Qi: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Meihan Liu: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Jiayi Yang: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Jiulin Zhou: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Lihong Yu: Liaoning Agriculture and Rural Development Service Center, Shenyang 110034, China
Fenghua Yu: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Chunling Chen: College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Yonghuan Wang: Liaoning Agriculture and Rural Development Service Center, Shenyang 110034, China

Agriculture, 2025, vol. 15, issue 22, 1-20

Abstract: The leaf area index (LAI) is a key physiological parameter characterizing rice canopy structure and growth status. To face the limits of traditional destructive sampling, which is time-consuming, labor-intensive, and difficult to achieve large-scale dynamic detection, this study proposes a precise UAV-based hyperspectral inversion method for rice LAI using the fusion of Days After Transplantation and Meteorological Factors data (DATaMF). The study framework consisted of three key components: spectral preprocessing (smoothing-R SG , resampling-R RS , first derivative transformation-R FD ), spectral feature selection (SPA, CARS, Relief-F), and the construction and assessment of LAI inversion models (RF, ELM, XGBoost) that integrated DATaMF. The results show that (1) the three-level data preprocessing procedure—comprising R SG , R RS , and R FD —coupled with the feature subset selected by the CARS method, demonstrates strong performance in LAI inversion; (2) the incorporation of DATaMF significantly improves rice LAI estimation, leading to improved model accuracy and robustness; and (3) the optimal LAI inversion model is achieved with the RF-based CARS-R FD -DATaMF approach, yielding test set R 2 , RMSE, and RPD values of 0.8015, 0.5745, and 2.2857, respectively. In conclusion, the hyperspectral LAI inversion method developed in this study, which integrates DATaMF, significantly enhances the model’s accuracy and stability under small-sample conditions. This approach provides reliable technical support for efficient, precise, and dynamic monitoring of rice growth.

Keywords: LAI inversion; UAV; feature band selection; first derivative transformation; machine learning; rice (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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