MPB_2024v15n1

Molecular Plant Breeding 2024, Vol.15, No.1, 15-26 http://genbreedpublisher.com/index.php/mpb 24 Kuroki K., Yan K., Iwata H., Shimizu K., Tameshige T., Nasuda S., and Guo W., 2022, Development of a high-throughput field phenotyping rover optimized for size-limited breeding fields as open-source hardware, Breeding Science, 72: 66-74. https://doi.org/10.1270/jsbbs.21059 PMid:36045888 PMCid:PMC8987849 Mazur B., Krebbers E., and Tingey S., 1999, Gene discovery and product development for grain quality traits, Science, 285(5426): 372-375. https://doi.org/10.1126/science.285.5426.372 PMid:10411493 Montesinos-López O.A., Gonzalez H.N., Montesinos-López A., Daza-Torres M., Lillemo M., Montesinos-López J.C., Crossa J., 2022b, Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding, Plant Genome, 15(3): e20214. https://doi.org/10.1002/tpg2.20214 PMid:35535459 Montesinos-Lopez O.A., Montesinos-Lopez A., Acosta R., Varshney R.K., Bentley A., and Crossa J., 2022a, Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding, Plant Genome, 15(1): e20194. https://doi.org/10.1002/tpg2.20194 PMid:35170851 Nerkar G., Devarumath S., Purankar M., Kumar A., Valarmathi R., Devarumath R., and Appunu C., 2022, Advances in crop breeding through precision genome editing, Frontiers in Genetics, 13: 880195. https://doi.org/10.3389/fgene.2022.880195 PMid:35910205 PMCid:PMC9329802 Nkoulou L., Ngalle H., Cros D., Adje C., Fassinou N., Bell J., and Achigan-Dako E., 2022, Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species, Frontiers in Plant Science, 13: 953133. https://doi.org/10.3389/fpls.2022.953133 PMid:36388523 PMCid:PMC9650417 Pawełkowicz M.E., Skarzyńska A., Pląder W., and Przybecki Z., 2019, Genetic and molecular bases of cucumber (Cucumis sativus L.) sex determination, Molecular Breeding, 39: 1-27. https://doi.org/10.1007/s11032-019-0959-6 Rafalski J., and Tingey S., 1993, Genetic diagnostics in plant breeding: RAPDs, microsatellites and machines, Trends in Genetics : TIG, 9(8): 275-280. https://doi.org/10.1016/0168-9525(93)90013-8 PMid:8104363 Sagan V., Maimaitijiang M., Paheding S., Bhadra S., Gosselin N., Burnette M., Demieville J., Hartling S., LeBauer D., Newcomb M., Pauli D., Peterson K., Shakoor N., Stylianou A., Zender C., and Mockler T., 2022, Data-driven artificial intelligence for calibration of hyperspectral big data, IEEE Transactions on Geoscience and Remote Sensing, 60: 1-20. https://doi.org/10.1109/TGRS.2021.3091409 Schaeffer S., and Nakata P., 2015, CRISPR/Cas9-mediated genome editing and gene replacement in plants: transitioning from lab to field, Plant Science: An International Journal of Experimental Plant Biology, 240: 130-142. https://doi.org/10.1016/j.plantsci.2015.09.011 PMid:26475194 Shailani A., Joshi R., Singla-Pareek S., and Pareek A., 2020, Stacking for future: pyramiding genes to improve drought and salinity tolerance in rice, Physiologia Plantarum, 172(2): 1352-1362. https://doi.org/10.1111/ppl.13270 PMid:33180968 Shorter R., Lawn R., and Hammer G., 1991, Improving genotypic adaptation in crops–a role for breeders, physiologists and modellers, Experimental Agriculture, 27: 155-175. https://doi.org/10.1017/S0014479700018810 Silva Júnior A.C.da., Sant’Anna I.C., Silva G.N., Cruz C.D., Nascimento M., Lopes L.B., and Soares P.C., 2023, Computational intelligence to study the importance of characteristics in flood-irrigated rice, Acta Scientiarum. Agronomy, 45: e57209. https://doi.org/10.4025/actasciagron.v45i1.57209 Smith K., Handelsman J., and Goodman R., 1997, Modeling dose-response relationships in biological control: partitioning host responses to the pathogen and biocontrol agent, Phytopathology, 87(7): 720-729. https://doi.org/10.1094/PHYTO.1997.87.7.720 PMid:18945094 Soltis P.S., Nelson G., Zare A., and Meineke E.K., 2020, Plants meet machines: prospects in machine learning for plant biology, Applications in Plant Sciences, 8(6): e11371. https://doi.org/10.1002/aps3.11371 PMCid:PMC7328654 Tayade R., Yoon J., Lay L., Khan A., Yoon Y., and Kim Y., 2022, Utilization of spectral indices for high-throughput phenotyping, Plants, 11(13): 1712. https://doi.org/10.3390/plants11131712 PMid:35807664 PMCid:PMC9268975

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