EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 216 (2025) SHS Web Conf., 216 (2025) 01046 References
Open Access
Issue
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
Article Number 01046
Number of page(s) 12
Section Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development
DOI https://doi.org/10.1051/shsconf/202521601046
Published online 23 May 2025
  1. R.Z. Naqvi, H.A. Siddiqui, M.A. Mahmood, S. Najeebullah, A. Ehsan, M. Azhar, A. Asif, Smart breeding approaches in the post-genomics era for developing climate-resilient food crops. Front. Plant Sci. 13, 972164 (2022). https://doi.org/10.3389/fpls.2022.972164 [CrossRef] [Google Scholar]
  2. C. Sun, H. Hu, Y. Cheng, X. Yang, Q. Qiao, C. Wang, F. Chen, Genomics‐assisted breeding: the next‐generation wheat breeding era. Plant Breed. 142(3), 259–268 (2023). https://doi.org/10.1111/pbr.13094 [CrossRef] [Google Scholar]
  3. Y. Fan, S. Shabala, Y. Ma, R. Xu, M. Zhou, Using QTL mapping to investigate the relationships between abiotic stress tolerance (drought and salinity) and agronomic and physiological traits. BMC Genomics 16, 1–11 (2015). https://doi.org/10.1186/s12864-015-1243-8 [CrossRef] [PubMed] [Google Scholar]
  4. A.R. Mapari, S. Mehandi, Enhancing Crop Resilience: Advances and Challenges in Marker-Assisted Selection for Disease Resistance. J. Adv. Biol. Biotechnol. 27(7), 569–580 (2024) [CrossRef] [Google Scholar]
  5. D.K. Saini, Y. Chopra, J. Singh, K.S. Sandhu, A. Kumar, S. Bazzer, P. Srivastava, A comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies. Mol. Breed. 42, 1–52 (2022). https://doi.org/10.1007/s11032-021-01272-7 [CrossRef] [Google Scholar]
  6. J. Ahlinder, D. Hall, M. Suontoma, M.J. Sillanpää, Principal component analysis revisited: fast multi-trait genetic evaluations with smooth convergence. bioRxiv, 2024-06 (2024) [Google Scholar]
  7. N. Prateep-Na-Thalang, P. Tongyoo, C. Phumichai, J. Duangjit, Comparing different statistical models for association mapping and genomic prediction of fruit quality traits in tomatoes. Sci. Hortic. 327, 112838 (2024). https://doi.org/10.1016/j.scienta.2023.112838 [CrossRef] [Google Scholar]
  8. I. Chang-Brahim, L.J. Koppensteiner, L. Beltrame, G. Bodner, A. Saranti, J. Salzinger, E.M. Molin, Reviewing the essential roles of remote phenotyping, GWAS, and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding. Front. Plant Sci. 15, 1319938 (2024). https://doi.org/10.3389/fpls.2024.1319938 [CrossRef] [Google Scholar]
  9. A. Alemu, J. Åstrand, O.A. Montesinos-Lopez, J.I.Y. Sanchez, J. Fernandez-Gonzalez, W. Tadesse, A. Chawade, Genomic selection in plant breeding: Key factors shaping two decades of progress. Mol. Plant (2024). https://doi.org/10.1016/j.molp.2024.02.012 [Google Scholar]
  10. R.L. Chavhan, V.R. Hinge, D.J. Wankhade, A.S. Deshmukh, N. Mahajan, U.S. Kadam, Bioinformatics for Molecular Breeding and Enhanced Crop Performance: Applications and Perspectives. Bioinformatics Plant Res. Crop Breed., 21–74 (2024). https://doi.org/10.1002/9781394209965.ch2 [CrossRef] [Google Scholar]
  11. A.K. Chandra, A. Kumar, A. Bharati, R. Joshi, A. Agrawal, S. Kumar, Microbial-assisted and genomic-assisted breeding: a two way approach for the improvement of nutritional quality traits in agricultural crops. 3 Biotech 10(1), 2 (2020). https://doi.org/10.1007/s13205-019-1994-z [CrossRef] [Google Scholar]
  12. C. Yin, H. Shi, P. Zhou, Y. Wang, X. Tao, Z. Yin, Y. Liu, Genomic Prediction of Growth Traits in Yorkshire Pigs of Different Reference Group Sizes Using Different Estimated Breeding Value Models. Animals 14(7), 1098 (2024). https://doi.org/10.3390/ani14071098 [CrossRef] [Google Scholar]
  13. M. Saeidi, A. Ahmadi, F. Moradi, M.R. Hajirezaei, Comparative metabolome profiling of two contrasting wheat cultivars in late-season water deficit. Front. Plant Physiol. 2, 1386473 (2024). https://doi.org/10.3389/fphgy.2024.1386473 [CrossRef] [Google Scholar]
  14. E.K. Ruby, G. Amirthayogam, G. Sasi, T. Chitra, A. Choubey, S. Gopalakrishnan, Advanced Image Processing Techniques for Automated Detection of Healthy and Infected Leaves in Agricultural Systems. Mesopotamian J. Comput. Sci., 62–70 (2024) [Google Scholar]
  15. Takele, T. Feyissa, T. Disasa, High-density linkage map and QTL map for yield and yield components of sorghum recombinant inbreed lines developed from Gambella× Sorcoll 163/07 using GBS. Cereal Res. Commun. 52(2), 383–396 (2024). https://doi.org/10.1007/s42976-023-00427-w [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.