Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index

• Published in: Agronomy (Basel) Vol. 14; no. 6; p. 1309
• Main Authors: Liu, Xiaochi, Li, Zhijun, Xiang, Youzhen, Tang, Zijun, Huang, Xiangyang, Shi, Hongzhao, Sun, Tao, Yang, Wanli, Cui, Shihao, Chen, Guofu, Zhang, Fucang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2024
• Subjects: Accuracy; Agricultural production; Agriculture; Back propagation networks; Chlorophyll; chlorophyll content; Continuous wavelet transform; Correlation analysis; Correlation coefficient; Correlation coefficients; Data collection; Fertilization; Fertilizers; Genetic algorithms; hyperspectral; Learning algorithms; Machine learning; Matrix methods; Neural networks; Nitrogen; optimal spectral index; Potassium; Precision farming; Prediction models; Reflectance; Remote sensing; Root-mean-square errors; Support vector machines; Triticum aestivum; Vegetation; wavelet transform; Wavelet transforms; Wheat; Winter wheat; China; United States > US
• DOI: 10.3390/agronomy14061309
• EISSN: 2073-4395

Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 21 to 210, and the correlation matrix method was utilized to identify the spectral index at each scale, with the highest correlation to winter wheat chlorophyll content as the optimal spectral index combination input. Subsequently, winter wheat chlorophyll content prediction models were developed using three machine learning methods: random forest (RF), support vector machine (SVM), and a genetic algorithm-optimized backpropagation neural network (GA-BP). The results indicate that the spectral data processed through continuous wavelet transform at seven scales, from 21 to 27, show the highest correlation with winter wheat chlorophyll content at a scale of 26, with a correlation coefficient of 0.738, compared with the correlation of 0.611 of the original reflectance, and the accuracy is improved by 20.7%. The average highest correlation value between the spectral index at scale 26 and winter wheat chlorophyll content is 0.752. As the scale of wavelet transform increases, the correlation between the spectral index and winter wheat chlorophyll content and the accuracy of the predictive model show a trend of first increasing and then decreasing. The optimal input variables for predicting winter wheat chlorophyll content and the best machine learning method are the spectral data at a scale of 26 processing combined with the GA-BP model. The optimal predictive model has a validation set coefficient of determination (R2) of 0.859, root mean square error (RMSE) of 1.366, and mean relative error (MRE) of 2.920%. The results show that the prediction model can provide a technical basis for improving the hyperspectral inversion accuracy of winter wheat chlorophyll and modern precision agriculture.