MGG_2025v16n5

Maize Genomics and Genetics 2025, Vol.16, No.5, 239-250 http://cropscipublisher.com/index.php/mgg 249 Luo Y., Wang H., Cao J., Li J., Tian Q., Leng G., and Niyogi D., 2024, Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought, Precision Agriculture, 25: 1982-2006. https://doi.org/10.1007/s11119-024-10149-6 Måløy H., Windju S., Bergersen S., Alsheikh M., and Downing K., 2021, Multimodal performers for genomic selection and crop yield prediction, Smart Agricultural Technology, 1: 100017. https://doi.org/10.1016/j.atech.2021.100017 McBreen J., Babar M., Jarquín D., Ampatzidis Y., Khan N., Kunwar S., Acharya J., Adewale S., and Brown-Guedira G., 2025, Enhancing genomic‐based forward prediction accuracy in wheat by integrating UAV‐derived hyperspectral and environmental data with machine learning under heat‐stressed environments, The Plant Genome, 18(1): e20554. https://doi.org/10.1002/tpg2.20554 Miao L., Zou Y., Cui X., Kattel G., Shang Y., and Zhu J., 2024, Predicting China's maize yield using multi-source datasets and machine learning algorithms, Remote Sensing, 16(13): 2417. https://doi.org/10.3390/rs16132417 Ndlovu N., Gowda M., Beyene Y., Chaikam V., Nzuve F., Makumbi D., McKeown P., Spillane C., and Prasanna B., 2024, Genomic loci associated with grain yield under well-watered and water-stressed conditions in multiple bi-parental maize populations, Frontiers in Sustainable Food Systems, 8: 1391989. https://doi.org/10.3389/fsufs.2024.1391989 Nepolean T., Kaul J., Mukri G., and Mittal S., 2018, Genomics-enabled next-generation breeding approaches for developing system-specific drought tolerant hybrids in maize, Frontiers in Plant Science, 9: 361. https://doi.org/10.3389/fpls.2018.00361 Saimon M., Moniruzzaman M., Islam M., Ahmed M., Rahaman M., Hossain S., and Manik T., 2023, Integrating genomic selection and machine learning: a data-driven approach to enhance corn yield resilience under climate change, Journal of Environmental and Agricultural Studies, 4(2): 20-27. https://doi.org/10.32996/jeas.2023.4.2.6 Shahhosseini M., Martinez-Feria R., Hu G., and Archontoulis S., 2019, Maize yield and nitrate loss prediction with machine learning algorithms, Environmental Research Letters, 14: 124026. https://doi.org/10.1088/1748-9326/ab5268 Sheoran S., Kaur Y., Kumar S., Shukla S., Rakshit S., and Kumar R., 2022, Recent advances for drought stress tolerance in maize (Zea mays L.): present status and future prospects, Frontiers in Plant Science, 13: 872566. https://doi.org/10.3389/fpls.2022.872566 Shikha M., Kanika A., Rao A., Mallikarjuna M., Gupta H., and Nepolean T., 2017, Genomic selection for drought tolerance using genome-wide SNPs in maize, Frontiers in Plant Science, 8: 550. https://doi.org/10.3389/fpls.2017.00550 Shook J., Gangopadhyay T., Wu L., Ganapathysubramanian B., Sarkar S., and Singh A., 2020, Crop yield prediction integrating genotype and weather variables using deep learning, PLoS ONE, 16(6): e0252402. https://doi.org/10.1371/journal.pone.0252402 Shuai G., and Basso B., 2022, Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models, Remote Sensing of Environment, 272: 112938. https://doi.org/10.1016/j.rse.2022.112938 Sirsat M., Oblessuc P., and Ramiro R., 2022, Genomic prediction of wheat grain yield using machine learning, Agriculture, 12(9): 1406. https://doi.org/10.3390/agriculture12091406 Széles A., Horváth É., Simon K., Zagyi P., and Huzsvai L., 2023, Maize production under drought stress: nutrient supply, yield prediction, Plants, 12(18): 3301. https://doi.org/10.3390/plants12183301 Tahi S., Hounmenou C., Houndji V., and KakaïR., 2024, An experimental analysis of traditional machine learning algorithms for maize yield prediction, Contemporary Mathematics, 5(4): 6208-6224. https://doi.org/10.37256/cm.5420244481 Tesfaye K., Sonder K., Cairns J., Magorokosho C., Tarekegn A., Kassie G., Getaneh F., Abdoulaye T., Abate T., and Erenstein O., 2016, Targeting drought-tolerant maize varieties in Southern Africa: a geospatial crop modeling approach using big data, The International Food and Agribusiness Management Review, 19: 1-18. Togninalli M., Wang X., Kucera T., Shrestha S., Juliana P., Mondal S., Pinto F., Govindan V., Crespo-Herrera L., Huerta-Espino J., Singh R., Borgwardt K., and Poland J., 2023, Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics, Bioinformatics, 39(6): btad336. https://doi.org/10.1093/bioinformatics/btad336 Tong H., and Nikoloski Z., 2020, Machine learning approaches for crop improvement: leveraging phenotypic and genotypic big data, Journal of Plant Physiology, 257: 153354. https://doi.org/10.1016/j.jplph.2020.153354 Van Klompenburg T., Kassahun A., and Catal C., 2020, Crop yield prediction using machine learning: a systematic literature review, Computers and Electronics in Agriculture, 177: 105709. https://doi.org/10.1016/j.compag.2020.105709

RkJQdWJsaXNoZXIy MjQ4ODYzNA==