Winter Wheat SPAD Prediction Based on Multiple Preprocessing, Sequential Module Fusion, and Feature Mining Methods

Ying Nian, Xiangxiang Su, Hu Yue, Sumera Anwar, Jun Li, Weiqiang Wang, Yali Sheng, Qiang Ma, Jikai Liu and Xinwei Li ()
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Ying Nian: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Xiangxiang Su: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Hu Yue: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Sumera Anwar: Department of Botany, Government College Women University Faisalabad, Faisalabad 38000, Pakistan
Jun Li: 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
Yali Sheng: College of Resource and Environment, Anhui Science and Technology University, Chuzhou 233100, China
Qiang Ma: 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, 2024, vol. 14, issue 12, 1-21

Abstract: Chlorophyll is a crucial indicator for monitoring crop growth and assessing nutritional status. Hyperspectral remote sensing plays an important role in precision agriculture, offering a non-destructive approach to predicting leaf chlorophyll. However, crop canopy spectra often face background noise and data redundancy challenges. To tackle these issues, this study develops an integrated processing strategy incorporating multiple preprocessing techniques, sequential module fusion, and feature mining methods. Initially, the original spectrum (OS) from 2021, 2022, and the fusion year underwent preprocessing through Fast Fourier Transform (FFT) smoothing, multiple scattering correction (MSC), the first derivative (FD), and the second derivative (SD). Secondly, feature mining was conducted using Competitive Adaptive Reweighted Sampling (CARS), Iterative Retention of Information Variables (IRIV), and Principal Component Analysis (PCA) based on the optimal preprocessing order module fusion data. Finally, Partial Least Squares Regression (PLSR) was used to construct a prediction model for winter wheat SPAD to compare the prediction effects in different years and growth stages. The findings show that the preprocessing sequential module fusion of FFT-MSC (firstly pre-processing using FFT, and secondly secondary processing of FFT spectral data using MSC) effectively reduced issues such as noisy signals and baseline drift. The FFT-MSC-IRIV-PLSR model (based on the combined FFT-MSC preprocessed spectral data, feature screening using IRIV, and then combining with PLSR to construct a prediction model) predicts SPAD with the highest overall accuracy, with an R 2 of 0.79–0.89, RMSE of 4.51–5.61, and MAE of 4.01–4.43. The model performed best in 2022, with an R 2 of 0.84–0.89 and RMSE of 4.51–6.74. The best prediction during different growth stages occurred in the early filling stage, with an R 2 of 0.75 and RMSE of 0.58. On the basis of this research, future work will focus on optimizing the data processing process and incorporating richer environmental data, so as to further enhance the predictive capability and applicability of the model.

Keywords: hyperspectral; winter wheat; chlorophyll; feature mining methods; preprocessing methods (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: 2024
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