Winter wheat chlorophyll content retrieval based on machine learning using in situ hyperspectral data

Abstract

Chlorophyll is the basic component of plant leaves. It is closely related to the use of solar radiation, the absorption of atmospheric carbon dioxide and the process of photosynthesis. The level of chlorophyll content has a very important indication of photosynthetic efficiency and development status of plants. Because hyperspectral data has the problem of large amount of data and high redundancy, this paper deals with hyperspectral data from two aspects to establish a prediction model of leaf chlorophyll content in different growth stages of winter wheat. On the one hand, this article does not perform dimensionality reduction processing on hyperspectral data. That is, for the full-band spectral data, the Ridge regression algorithm (abbreviated as Ridge) is used to establish a multiple linear prediction model. Use the Gradient Boosting Regression Tree algorithm (abbreviated as GBRT) and the feedforward neural network based on Back Propagation algorithm (abbreviated as BP) to establish two multiple nonlinear prediction models. On the other hand, dimensionality reduction is performed on hyperspectral data. Use Elastic Net algorithm (abbreviated as EN) to reduce the dimensionality of hyperspectral data to extract sensitive bands, and then use GBRT algorithm and feedforward neural network based on Back Propagation algorithm to establish multivariate nonlinear prediction models (abbreviated as EN-GBRT, EN-BP). After the model accuracy test, the conclusions are as follows: (1) The nonlinear prediction model obtained by the gradient boosting regression tree algorithm has higher accuracy and universality than the linear model in each growth period of winter wheat (R2GBRT＞R2Ridge). (2) The EN-GBRT prediction model has greater advantages in predicting the chlorophyll content of winter wheat at each growth stage (R2EN-GBRT＞R2EN-BP) within two nonlinear prediction models. (3) Comprehensive comparison of the prediction accuracy of GBRT and EN-GBRT models in each growth period. In the jointing period, heading period, flowering period, filling period, milking period and maturity period of winter wheat, the accuracy of prediction using the GBRT model was higher (R2jointing = 0.81, R2heading = 0.50, R2flowering = 0.74, R2filling = 0.41, R2milking = 0.84, R2maturity = 0.95). In the whole growth period of winter wheat, the prediction accuracy of EN-GBRT model was better (R2whole = 0.85).