基于无人机影像的冠层光谱和结构特征监测甜菜长势

A sugar beet is one of the most important cash crops in northern China. It is a high demand for the rapid, accurate, and high-throughput acquisition of the fresh weight of aboveground and root, the sugar content of root, and the chlorophyll content of aboveground in the production of sugar beet. An Unmanned Aerial Vehicle (UAV) can serve as a significant approach, due to its flexibility, low cost, and high spatiotemporal resolution. In this study, a UAV equipped with digital and multispectral cameras was utilized to capture the images of sugar beet during the leaf clusters, root tuber, sugar growth, and accumulation period, thereby extracting the structural and spectral characteristics of the canopy. The estimation models were also established for the various indexes using the Random Forest Regression (RFR) and Partial Least Squares Regression (PLSR), including the fresh weight of shoot and root tuber, the sugar content of root tuber, and Soil Plant Analysis Development (SPAD) value during the whole period of sugar beet. The results showed that the RFR and PLSR model performed well to predict the fresh weight and sugar content of shoot and root tuber, with the coefficient of determination R² ranging from 0.9 to 0.94 and from 0.88 to 0.9, respectively, while the relative Root Mean Square Error (rRMSE) ranging from 7.6% to 17% and from 8.8% to 20%, respectively. Both models presented weak predictions for the SPAD values, where the R² values were only 0.66 and 0.67, respectively. Furthermore, a Permutation Importance (PIMP) was used to screen the more sensitive variables with the dominated impacts on the prediction, in order to reduce the size of the input variable set for the less cost and complexity of data collection. As such, the optimal prediction models of RFR and PLSR were achieved for the growth monitoring of sugar roots. It was found that excellent predictions were achieved on the fresh weight and sugar content of shoot and root tuber, with the R² value ranging from 0.89 to 0.94, and from 0.74 to 0.91, respectively, and the rRMSE value ranging from 7.3% to 19% and from 7.6% to 19%, respectively. Nevertheless, the RFR and PLSR model presented weak predictions for the SPAD values, where the R² values were only 0.65 and 0.68, respectively. Correspondingly, the accuracy of the RFR model was slightly better than that of the PLSR model. More importantly, the PIMP variable screening can be widely expected to reduce the complexity of data collection with optimal accuracy. Consequently, the canopy structure and spectral features obtained by UAVs can be utilized to quickly and accurately monitor the growth and sugar content of sugar beet. The finding can provide a strong reference to estimate the root active substances of tubers crops using UAV proximity.