Estimating Wheat Chlorophyll Content Using a Multi-Source Deep Feature Neural Network

Jun Li, Yali Sheng, Weiqiang Wang, Jikai Liu () and Xinwei Li ()
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Jun Li: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Yali Sheng: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Weiqiang Wang: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Jikai Liu: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Xinwei Li: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China

Agriculture, 2025, vol. 15, issue 15, 1-19

Abstract: Chlorophyll plays a vital role in wheat growth and fertilization management. Accurate and efficient estimation of chlorophyll content is crucial for providing a scientific foundation for precision agricultural management. Unmanned aerial vehicles (UAVs), characterized by high flexibility, spatial resolution, and operational efficiency, have emerged as effective tools for estimating chlorophyll content in wheat. Although multi-source data derived from UAV-based multispectral imagery have shown potential for wheat chlorophyll estimation, the importance of multi-source deep feature fusion has not been adequately addressed. Therefore, this study aims to estimate wheat chlorophyll content by integrating spectral and textural features extracted from UAV multispectral imagery, in conjunction with partial least squares regression (PLSR), random forest regression (RFR), deep neural network (DNN), and a novel multi-source deep feature neural network (MDFNN) proposed in this research. The results demonstrate the following: (1) Except for the RFR model, models based on texture features exhibit superior accuracy compared to those based on spectral features. Furthermore, the estimation accuracy achieved by fusing spectral and texture features is significantly greater than that obtained using a single type of data. (2) The MDFNN proposed in this study outperformed other models in chlorophyll content estimation, with an R 2 of 0.850, an RMSE of 5.602, and an RRMSE of 15.76%. Compared to the second-best model, the DNN (R 2 = 0.799, RMSE = 6.479, RRMSE = 18.23%), the MDFNN achieved a 6.4% increase in R 2 , and 13.5% reductions in both RMSE and RRMSE. (3) The MDFNN exhibited strong robustness and adaptability across varying years, wheat varieties, and nitrogen application levels. The findings of this study offer important insights into UAV-based remote sensing applications for estimating wheat chlorophyll under field conditions.

Keywords: remote sensing; UAV; chlorophyll content; wheat; deep learning; deep feature; multi-source data (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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