QTL Mapping of Leaf Area Index and Chlorophyll Content Based on UAV Remote Sensing in Wheat

High-throughput phenotypic identification is a prerequisite for large-scale and gene mining of important traits. However, existing work has rarely leveraged high-throughput into quantitative trait locus (QTL) acquisition in wheat crops. Clarifying the feasibility effectiveness data obtained from UAV multispectral images traits an urgent problem to be solved wheat. In this paper, 309 lines spring Worrakatta × Berkut recombinant inbred line (RIL) were taken as materials. First, we leaf area index (LAI) including flowering, filling, mature stages, well flag chlorophyll content (CC) heading, filling under normal irrigation drought stress, respectively. Then, on basis normalized difference vegetation (NDVI) green (GNDVI), which determined by imagery, LAI CC comprehensively estimated through classification regression tree (CART) cross-validation algorithms. Finally, identified QTLs analyzing predicted measured values. The results show that values determination coefficient (R2) ranged 0.79 0.93, root-mean-square error (RMSE) 0.30 1.05, relative (RE) 0.01 0.18. Furthermore, correlation coefficients 0.93 0.94 0.80 0.92 at different growth stages stress. Additionally, linkage map RIL population was constructed 11,375 SNPs; eight detected chromosomes 1BL, 2BL (four QTLs), 3BL, 5BS, 5DL, three 1DS (two QTLs) 3AL. closely linked formed two regions chromosome (from 54 56 cM 96 101 cM, respectively) one region 26 27 cM). Each QTL explained variation 2.5% 13.8% 5.8%. For LAI, flowering stage, maturity among QLAI.xjau-5DL-pre both stages. CC, heading stage QCC.xjau-1DS Three (QLAI.xjau-2BL-pre.2, QLAI.xjau-2BL.2, QLAI.xjau-3BL-pre) stress conditions. Five imagery-predicted values, while four manual measurement Lastly, investigations these reference genome 10 candidate genes associated with belonging F-box family proteins, peroxidase, GATA transcription factor, C2H2 zinc finger structural protein, etc., are involved regulation crop development, signal transduction, response These findings reveal sensing technology relatively high reliability phenotyping can play role genetic improvement.