Non-destructive time-series assessment of chlorophyll content in flag-leaf (FLC) accurately mimics the senescence rate and the identification of genetic loci associated with senescence provides valuable knowledge to improve yield stability under stressed environments. In this study, we employed both unmanned aerial vehicles (UAVs) equipped with red–green–blue (RGB) camera and ground-based SPAD-502 instrument to conduct temporal phenotyping of senescence. A total of 262 recombinant inbred lines derived from the cross of Zhongmai 578/ Jimai 22 were evaluated for senescence-related traits across three environments, spanning from heading to 35 d post-anthesis. The manual senescence rate (MSR) was quantified using the FLC and the active accumulated temperature, and UAV derived vegetation index were utilized to assess the stay-green rate (USG) facilitating the identification of senescent and stay-green lines. Results indicated that higher senescence rates significantly impacted grain yield, primarily by influencing thousand-kernel weight, and plant height. Quantitative trait loci (QTL) mapping for FLC, USG, and MSR using the 50K SNP array identified 38 stable loci associated with RGB-based vegetation indices and senescence-related traits: among which 19 loci related to senescence traits from UAV and FLC were consistently detected across at least two growth stages, with nine loci likely representing novel QTL. This study highlights the potential of UAV-based high-throughput phenotyping and phenology in identifying critical loci associated with senescence rates in wheat, validating the relationship between senescence rates and yield-related traits in wheat, offering valuable opportunities for gene discovery and significant applications in breeding programs.

The spike number (SN) is an important trait that significantly impacts grain yield in wheat. Manual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting. A YOLOX algorithm was used to determine the optimal growth stage for developing wheat spike detection models among recombinant inbred lines (RILs) across Zhongmai 175 × Lunxuan 987 and a diverse panel of 166 cultivars. We subsequently increased the precision of spike identification by developing a new YOLOX-P algorithm that incorporates the convolutional block attention module and increasing the resolution of the input images. We also used these SN data to identify underlying loci in the Zhongmai 578 × Jimai 22 RIL population. The results revealed that the late grain-filling stage presented the highest precision among the SN detection models, with accuracies ranging from 91.8 to 95.02 %. The improved YOLOX-P algorithm demonstrated higher mean average precision scores (5.30−5.99 %) and F1 scores (0.06) than did the YOLOX algorithm when it was applied to the same subsets. Three new SN loci, namely, QSN.caas-4A2, QSN.caas-4D and QSN.caas-5B2, were identified using the 50k SNP arrays. Two kompetitive allele-specific PCR markers linked with QSN.caas-4A2 and QSN.caas-5B2 were developed, and their genetic effects were validated in a diverse panel of 166 cultivars. These findings provide useful tools for high-throughput identification of SNs and novel loci in wheat.

In quantitative genomic analysis of wheat plant height (PH), the average height of a few representative plants is typically used to represent the PH of the entire plot, which overlooks the variation in height among other plants. Extracting different height quantiles from canopy point clouds can address this limitation. For this purpose, low-cost UAV cross-circling oblique (CCO) imaging, combined with structure-from-motion (SfM) and multi-view stereopsis (MVS), was employed to generate precise canopy point clouds for 262 F5 recombinant inbred lines (Zhongmai 578 ​× ​Jimai 22) across seven environments. Multi-level 3D-PH measurements were extracted from six height quantiles, revealing a strong correlation (mean r ​= ​0.95) between 3D-PH and field-measured PH (FM-PH) across environments. The 90 ​% and 92 ​% height quantiles showed the closest agreement with FM-PH compared to other quantiles. Eleven stable quantitative trait loci (QTLs) associated with multi-level 3D-PH were identified using a 50K single nucleotide polymorphism array. Among these, QPhzj.caas-3A.2 (detected by 3D-PH) and QPhzj.caas-7A.1 (detected by both FM-PH and 3D-PH) represented potential novel loci. KASP markers for these QTLs were developed and validated. Furthermore, within the intervals of QPhzj.caas-5A and QPhzj.caas-3B (both were detected by 3D-PH), two candidate genes associated with PH regulation were identified: TaGL3-5A and Rht5, respectively. Corresponding KASP markers for these genes were also developed and validated. This study highlighted the advantages of 3D model and multi-level 3D-PH in elucidating the genetic basis of crop height, and provided a precise and objective basis for advancing wheat breeding programs.

Spike number (SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks (Faster R-CNN) on Red-Green-Blue (RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660K SNP array. Analysis of manual spike number (MSN) in the field, image-based spike number (ISN), and verification of spike number (VSN) by Faster R-CNN revealed significant variation (P < 0.001) among genotypes, with high heritability ranged from 0.71 to 0.96. The coefficients of determination (R²) between ISN and VSN was 0.83, which was higher than that between ISN and MSN (R² = 0.51), and between VSN and MSN (R² = 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL Qsnyz.caas-4DS, Qsnyz.caas-7DS, and QSnyz.caas-7DL were identified based on MSN, ISN and VSN data, while QSnyz.caas-7DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat.