Dynamic UAV Phenotyping for Rice Disease Resistance Analysis Based on Multisource Data

of 0.65. Moreover, model updating strategy was used to explore the scalability of the established model in different geographical locations. Twenty percent of transferred data for model training was useful for the evaluation of disease severity over different sites. In addition, the method for phenotypic analysis of rice disease we built here was combined with quantitative trait loci (QTL) analysis to identify resistance QTL in genetic populations at different growth stages. Three new QTLs were identified, and QTLs identified at different growth stages were inconsistent. QTL analysis combined with UAV high-throughput phenotyping provides new ideas for accelerating disease resistance breeding.