Estimation of sugar content in sugar beet root based on UAV multi-sensor data

Sugar beet (Beta vulgaris L.) is the second largest sugar source crop in the world. Sugar content in its beetroot is an important quality index of sugar beet. Rapid and accurate estimation of sugar content in beetroot is helpful to high-throughput phenotype and genotype breeding. The purpose of the current study is to explore the potential of UAV-based multimodal remote sensing data in sugar content estimation. Multimodal data were collected by different UAV-platform carried with RGB, multi-spectral, thermal infrared, hyperspectral and LiDAR. Spectral, structure and thermal features of canopy were extracted from different sensor combinations to estimate sugar content during sugar accumulation period of beetroot by using Partial Least Squares Regression (PLSR), Bayesian Ridge Regression (BRR) and Support Vector Regression (SVR). The main results are as follows: (i) In estimating sugar content, published vegetation indices (PBI, NDVI, TCARI, etc.) calculated from hyperspectral as spectral features outperformed these of multispectral. Structure features calculated from LiDAR outperformed those from RGB. (ii) Eight newly developed vegetation indices presented better performance than published vegetation indices. Among them (R730-R734)/(R730 + R734) index using two bands and (R694-R714)/(R710-R714) using three bands presented the best performance. (iii) Multimodal data combination from different sensors improved estimation accuracy of sugar content than data from individual sensors. PLSR based multimodal data from LiDAR, hyperspectral, thermal infrared sensor presented the best accuracy with R² = 0.64 and relative root mean square error (rRMSE) = 7.2 %. These results showed that UAV-based canopy features from multimodal data can be used to quickly and accurately obtain sugar content of beetroot. This study provides a basic reference for estimating bioactive substances in roots of root tuber crops based on multimodal canopy remote sensing data.