Plant Gene and Trait 2024, Vol.15, No.1, 1-7 http://genbreedpublisher.com/index.php/pgt 7 At the same time, strengthening the research on the physiological and molecular links between drought and wheat growth and development will contribute to a deeper understanding of drought response mechanisms, so as to better optimize agricultural production strategies, and then apply machine learning technology to actual agricultural production to develop data-driven agricultural management measures to promote wheat drought resistance and improve yield and quality. These future research directions will push machine learning algorithms to play a more significant role in predicting wheat's response to drought, providing more reliable solutions to the challenges climate change poses to agriculture. Acknowledgments The author appreciates the feedback from two anonymous peer reviewers on the manuscript of this study. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Ahmed M.U., and Hussain I., 2022, Prediction of wheat production using machine learning algorithms in northern areas of Pakistan, Telecommunications Policy, 46(6): 102370. Ambarwari A., Adrian Q.J., and Herdiyeni Y., 2020, Analysis of the effect of data scaling on the performance of the machine learning algorithm for plant identification, Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1): 117-122. https://doi.org/10.29207/resti.v4i1.1517 Cai H.H., Peng J., Liu W.Y., Luo D.F., Wang Y.Z., Bai J.D., and Bai Z.J., 2021, Inversion and mapping of soil pH valve based on in-situ hyperspectral data in cotton field, Shuitu Baochi Tongbao (Bulletin of Soil and Water Conservation), 41(4): 189-195. Cao J., Zhang Z., Luo Y., Zhang L., Zhang J., Li Z., and Tao F., 2019, Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine, European Journal of Agronomy, 123: 126204. https://doi.org/10.1016/j.eja.2020.126204 Feng P., Wang B., Liu D.L., and Yu Q., 2019, Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia, Agricultural Systems, 173: 303-316. Mwadzingeni L., Shimelis H., Dube E., Laing M.D., and Tsilo T.J., 2016, Breeding wheat for drought tolerance: progress and technologies, Journal of Integrative Agriculture, 15(5): 935-943. https://doi.org/10.1016/S2095-3119(15)61102-9 Rijal B., Baduwal P., Chaudhary M., Chapagain S., Khanal S., Khanal S., and Poudel P.B., 2021, Drought stress impacts on wheat and its resistance mechanisms, Malaysian Journal of Sustainable Agriculture, 5(2): 67-76. Sallam A., Alqudah A.M., Dawood M.F.A., Baenziger P.S., and Börner A., 2019, Drought stress tolerance in wheat and barley: advances in physiology, breeding and genetics research, Int. J. Mol. Sci., 20(13): 3137. https://doi.org/10.3390/ijms20133137 PMid:31252573 PMCid:PMC6651786 Sundararajan K., Garg L., Srinivasan K., Bashir A.K., Kaliappan J., Ganapathy G.P., Selvaraj S.K., and Meena T., 2021, A contemporary review on drought modeling using machine learning approaches, Computer Modeling in Engineering and Sciences, 128 (2): 447-487. https://doi.org/10.32604/cmes.2021.015528 Zhang B.Y., Li X., and Zhang X.L., 2023, Influences of drought events on ecological resilience of Larix principis-rupprechtii and Pinus tabulaeformis, Hebei Nongye Daxue Xuebao (Journal of Agricultural University of Hebei), 46(4): 65-73. Zhang J.B., Xue X.P., Li N., Li H.Y., Zhang L., and Song J.P., 2019, Effects of drought stress on physiological characteristics and dry matter production of winter wheat during water critical period, Shamo yu Lüzhou Qixiang (Desert and Oasis Meteorology), 13(3): 124-130.
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