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

文献类型： 外文期刊

作者： Li, Lei 1 ; Hassan, Muhammad Adeel 4 ; Wang, Duoxia 1 ; Wan, Guoliang 1 ; Beegum, Sahila 4 ; Rasheed, Awais 1 ; Xia, Xianchun 1 ; He, Yong 3 ; Zhang, Yong 1 ; He, Zhonghu 1 ; Liu, Jindong 1 ; Xiao, Yonggui 1 ;

作者机构： 1.Chinese Acad Agr Sci CAAS, Inst Crop Sci, Natl Wheat Improvement Ctr, State Key Lab Crop Gene Resources & Breeding, Beijing 100081, Peoples R China

2.Chinese Acad Agr Sci, Zhongyuan Res Ctr, Xinxiang 453519, Peoples R China

3.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing 100081, Peoples R China

4.ARS, USDA, Adapt Cropping Syst Lab, Beltsville, MD 20705 USA

5.Oak Ridge Inst Sci & Educ, Oak Ridge, TN 37830 USA

6.Univ Nebraska, Robert B Daugherty Water Food Global Inst, Nebraska Water Ctr, 2021 Transformat Dr, Lincoln, NE 68588 USA

7.Quaid I Azam Univ, Dept Plant Sci, Islamabad, Pakistan

8.CAAS, Int Maize & Wheat Improvement Ctr CIMMYT, China Off, Beijing 100081, Peoples R China

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

期刊名称：PLANT PHENOMICS （ 影响因子：6.4； 五年影响因子：7.1 ）

ISSN： 2643-6515

年卷期： 2025 年 7 卷 2 期

页码：

收录情况： SCI

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 x 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 x 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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