MPB_2024v15n5

Molecular Plant Breeding 2024, Vol.15, No.5, 233-246 http://genbreedpublisher.com/index.php/mpb 245 Kuska M., Behmann J., Grosskinsky D., Roitsch T., and Mahlein A., 2018, Screening of barley resistance against powdery mildew by simultaneous high-throughput enzyme activity signature profiling and multispectral imaging, Frontiers in Plant Science, 9: 1074. https://doi.org/10.3389/fpls.2018.01074 Liu S., Hall M., Griffey C., and McKendry A., 2009, Meta-analysis of QTL associated with Fusarium head blight resistance in wheat, Crop Science, 49: 1955-1968. https://doi.org/10.2135/cropsci2009.03.0115 Miedaner T., and Korzun V., 2012, Marker-assisted selection for disease resistance in wheat and barley breeding, Phytopathology, 102(6): 560-566. https://doi.org/10.1094/PHYTO-05-11-0157 Moghimi A., Yang C., and Marchetto P., 2018, Ensemble feature selection for plant phenotyping: a journey from hyperspectral to multispectral imaging, IEEE Access, 6: 56870-56884. https://doi.org/10.1109/ACCESS.2018.2872801 Moreira F., Oliveira H., Volenec J., Rainey K., and Brito L., 2020, Integrating high-throughput phenotyping and statistical genomic methods to genetically improve longitudinal traits in crops, Frontiers in Plant Science, 11: 681. https://doi.org/10.3389/fpls.2020.00681 Ninomiya S., 2022, High-throughput field crop phenotyping: current status and challenges, Breeding Science, 72: 3-18. https://doi.org/10.1270/jsbbs.21069 Nogueira E., Oliveira B., Bulcão-Neto R., and Soares F., 2023, A systematic review of the literature on machine learning methods applied to high throughput phenotyping in agricultural production, IEEE Latin America Transactions, 21: 783-796. https://doi.org/10.1109/TLA.2023.10244177 Pour M., Fotouhi R., Hucl P., and Zhang Q., 2021, Development of a mobile platform for field-based high-throughput wheat phenotyping, Remote. Sens., 13: 1560. https://doi.org/10.3390/rs13081560 Pradhan A., Budhlakoti N., Mishra D., Prasad P., Bhardwaj S., Sareen S., Sivasamy M., Jayaprakash P., Geetha M., Nisha R., Shajitha P., Peter J., Kaur A., Kaur S., Vikas V., Singh K., and Kumar S., 2023, Identification of novel QTLs/defense genes in spring wheat germplasm panel for seedling and adult plant resistance to stem rust and their validation through KASP marker assays, Plant Disease, 107(6): 1847-1860. https://doi.org/10.1094/PDIS-09-22-2242-RE Rutkoski J., Poland J., Mondal S., Autrique E., Pérez L., Crossa J., Reynolds M., and Singh R., 2016, Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat, G3: Genes, Genomes, Genetics, 6: 2799-2808. https://doi.org/10.1534/g3.116.032888 Shakoor N., Lee S., and Mockler T., 2017, High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field, Current Opinion in Plant Biology, 38: 184-192. https://doi.org/10.1016/j.pbi.2017.05.006 Singh D., Wang X., Kumar U., Gao L., Noor M., Imtiaz M., Singh R., and Poland J., 2019, High-throughput phenotyping enabled genetic dissection of crop lodging in wheat, Frontiers in Plant Science, 10: 394. https://doi.org/10.3389/fpls.2019.00394 Smith D., Potgieter A., and Chapman S., 2021, Scaling up high-throughput phenotyping for abiotic stress selection in the field, Theoretical and Applied Genetics, 134: 1845-1866. https://doi.org/10.1007/s00122-021-03864-5 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 Tolley S., Yang Y., and Mohammadi M., 2020, High-throughput phenotyping identifies plant growth differences under well-watered and drought treatments, Journal of Integrative Agriculture, 19: 2429-2438. https://doi.org/10.1016/S2095-3119(20)63154-9 Wang X., Xuan H., Evers B., Shrestha S., Pless R., and Poland J., 2019, High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat, GigaScience, 8(11): giz120. https://doi.org/10.1101/527911 Wu W.C., 2024, Predicting wheat response to drought using machine learning algorithms, Plant Gene and Trait, 15(1): 1-7. https://doi.org/10.5376/pgt.2024.15.0001 Xie C., and Yang C., 2020, A review on plant high-throughput phenotyping traits using UAV-based sensors, Comput. Electron. Agric., 178: 105731. https://doi.org/10.1016/j.compag.2020.105731 Yang Y., Nan R., Mi T., Song Y., Shi F., Liu X., Wang Y., Sun F., Xi Y., and Zhang C., 2023, Rapid and nondestructive evaluation of wheat chlorophyll under drought stress using hyperspectral imaging, International Journal of Molecular Sciences, 24(6): 5825. https://doi.org/10.3390/ijms24065825

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