数字农科院2.0

RGB imaging and computer vision-based approaches for identifying spike number loci for wheat

文献类型： 外文期刊

作者： Lei Li;Muhammad Adeel Hassan;Duoxia Wang;Guoliang Wan;Sahila Beegum;Awais Rasheed;Xianchun Xia;Yong He;Yong Zhang;Zhonghu He;Jindong Liu;Yonggui Xiao

作者机构：

关键词： Common wheat;Imaging;KASP markers;Quantitative genetic analysis;Spike number

期刊名称： Plant Phenomics

ISSN： 2643-6515

年卷期： 2025 年 7 卷 2 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版） ; ; CSCD（2025-2026年度）

摘要： 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.

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