Monitoring the growth status of winter wheat by using the machine learning algorithm and the fusion of spectral and texture features derived from the UAV remote sensing

Remote sensing via unmanned aerial vehicle (UAV) could provide critical data support for estimating the real-time growth status of crops. In this study, the vegetation indexes (VI) and texture features (T) from multispectral images were extracted, and the entropy method was used to construct a comprehensive growth index (CGI) which ultimately reflected growth of winter wheat. Later on, the predictive models of winter wheat growth were established and evaluated by using the machine learning modeling methods of BPNN, RF and SVM with the different combinations of spectral and texture features. The results revealed that the correlation of CGI was improved compared with other single growth indicators, and it also reached a significant correlation level (r > 0.6) with the texture features based on the red edge band. For different input variables, the CGI estimation accuracy for most models based on the combination VI and T were superior than that of VI or T alone with the mean R² = 0.858; while the average values of R² of the models based on VI and T alone were 0.825 and 0.774 respectively. It also indicated that fusion of the spectral and texture features improved predictive performance of winter wheat growth. Among all the crop growth indicators, the CGI achieved the best performance as well by using the RF and VI + T variable (R² = 0.888, RMSE = 0.041, RPD = 2.989), which confirmed the application potential of RF machine learning method in estimating the winter wheat growth. Lastly, it also proved the feasibility of constructing comprehensive indicators to monitor wheat growth by entropy method as the fact that the estimated results of CGI models were also better than most of the single growth indicators with the mean R² = 0.819 for all the CGI models. This study is expected to offer both theoretical and practical references for monitoring the growth of winter wheat through UAV-based multispectral technology.