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Li, L., Hassan, M. A., Wang, D., Wan, G., Beegum, S., Rasheed, A., Xia, X., He, Y., Zhang, Y., He, Z., Liu, J., & Xiao, Y. (2025). RGB imaging and computer vision-based approaches for identifying spike number loci for wheat. Plant Phenomics, 7(2), 100051. https://doi.org/10.1016/j.plaphe.2025.100051
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Abstract
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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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Journal
Plant Phenomics
Journal volume
7
Journal issue
2
Article number
100051
Place of Publication
United States of America
Publisher
Elsevier B.V.
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National Natural Science Foundation of China
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Breeding for Tomorrow