Abstract
Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGI(ewm)) and FCE (CGI(fce)) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGI(fce) and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination (R(2)) values of ELM-CGI(ewm) and ELM- CGI(fce) increased by 20.83% and 20.37%, respectively. (3) The CGI(fce) inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGI(fce)), with R(2) reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGI(fce) (PSO-ELM-CGI(fce)), and the precision is significantly improved compared with that before optimization, with R(2) reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring.