RGB imaging and computer vision-based approaches for identifying spike number loci for wheat
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...
| Autores principales: | , , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179248 |
| _version_ | 1855533914072809472 |
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| author | Li, Lei Hassan, Muhammad Adeel Wang, Duoxia Wan, Guoliang Beegum, Sahila Rasheed, Awais Xia, Xianchun He, Yong Zhang, Yong He, Zhonghu Liu, Jindong Xiao, Yonggui |
| author_browse | Beegum, Sahila Hassan, Muhammad Adeel He, Yong He, Zhonghu Li, Lei Liu, Jindong Rasheed, Awais Wan, Guoliang Wang, Duoxia Xia, Xianchun Xiao, Yonggui Zhang, Yong |
| author_facet | Li, Lei Hassan, Muhammad Adeel Wang, Duoxia Wan, Guoliang Beegum, Sahila Rasheed, Awais Xia, Xianchun He, Yong Zhang, Yong He, Zhonghu Liu, Jindong Xiao, Yonggui |
| author_sort | Li, Lei |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace179248 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1792482025-12-24T02:12:07Z RGB imaging and computer vision-based approaches for identifying spike number loci for wheat Li, Lei Hassan, Muhammad Adeel Wang, Duoxia Wan, Guoliang Beegum, Sahila Rasheed, Awais Xia, Xianchun He, Yong Zhang, Yong He, Zhonghu Liu, Jindong Xiao, Yonggui triticum aestivum genetics spikes datasets 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. 2025-06 2025-12-23T17:37:24Z 2025-12-23T17:37:24Z Journal Article https://hdl.handle.net/10568/179248 en Open Access application/pdf Elsevier 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 |
| spellingShingle | triticum aestivum genetics spikes datasets Li, Lei Hassan, Muhammad Adeel Wang, Duoxia Wan, Guoliang Beegum, Sahila Rasheed, Awais Xia, Xianchun He, Yong Zhang, Yong He, Zhonghu Liu, Jindong Xiao, Yonggui RGB imaging and computer vision-based approaches for identifying spike number loci for wheat |
| title | RGB imaging and computer vision-based approaches for identifying spike number loci for wheat |
| title_full | RGB imaging and computer vision-based approaches for identifying spike number loci for wheat |
| title_fullStr | RGB imaging and computer vision-based approaches for identifying spike number loci for wheat |
| title_full_unstemmed | RGB imaging and computer vision-based approaches for identifying spike number loci for wheat |
| title_short | RGB imaging and computer vision-based approaches for identifying spike number loci for wheat |
| title_sort | rgb imaging and computer vision based approaches for identifying spike number loci for wheat |
| topic | triticum aestivum genetics spikes datasets |
| url | https://hdl.handle.net/10568/179248 |
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