To obtain the canopy chlorophyll content of winter wheat in a rapid and non-destructive high-throughput manner, the study was conducted on winter wheat in Xinjiang Manas Experimental Base in 2021, and the multispectral images of two water treatments' normal irrigation (NI) and drought stress (DS) in three key fertility stages (heading, flowering, and filling) of winter wheat were obtained by DJI P4M unmanned aerial vehicle (UAV). The flag leaf chlorophyll content (CC) data of different genotypes in the field were obtained by SPAD-502 Plus chlorophyll meter. Firstly, the CC distribution of different genotypes was studied, then, 13 vegetation indices, combined with the Random Forest algorithm and correlation evaluation of CC, and 14 vegetation indices were used for vegetation index preference. Finally, preferential vegetation indices and nine machine learning algorithms, Ridge regression with cross-validation (RidgeCV), Ridge, Adaboost Regression, Bagging_Regressor, K_Neighbor, Gradient_Boosting_Regressor, Random Forest, Support Vector Machine (SVM), and Least absolute shrinkage and selection operator (Lasso), were preferentially selected to construct the CC estimation models under two water treatments at three different fertility stages, which were evaluated by correlation coefficient (r), root means square error (RMSE) and the normalized root mean square error (NRMSE) to select the optimal estimation model. The results showed that the CC values under normal irrigation were higher than those underwater limitation treatment at different fertility stages; several vegetation indices and CC values showed a highly significant correlation, with the highest correlation reaching.51; in the prediction model construction of CC values, different models under normal irrigation and water limitation treatment had high estimation accuracy, among which the model with the highest prediction accuracy under normal irrigation was at the heading stage. The highest precision of the model prediction under normal irrigation was in the RidgeCV model (r = 0.63, RMSE = 3.28, NRMSE = 16.2%) and the highest precision of the model prediction under water limitation treatment was in the SVM model (r = 0.63, RMSE = 3.47, NRMSE = 19.2%).

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