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Applications of Artificial Intelligence in Wheat Breeding for Sustainable Food Security

文献类型： 外文期刊

作者： Mushtaq, Muhammad Ahtasham 1 ; Ahmed, Hafiz Ghulam Muhu-Din 1 ; Zeng, Yawen 2 ;

作者机构： 1.Islamia Univ Bahawalpur, Fac Agr & Environm, Dept Plant Breeding & Genet, Bahawalpur 63100, Pakistan

2.Yunnan Acad Agr Sci, Biotechnol & Germplasm Resources Inst, Kunming 650205, Peoples R China

关键词： artificial intelligence; wheat breeding; machine learning; genomic analysis; interdisciplinary

期刊名称：SUSTAINABILITY （ 影响因子：3.3； 五年影响因子：3.6 ）

ISSN：

年卷期： 2024 年 16 卷 13 期

页码：

收录情况： SCI

摘要： In agriculture, especially in crop breeding, innovative approaches are required to address the urgent issues posed by climate change and global food security. Artificial intelligence (AI) is a revolutionary technology in wheat breeding that provides new approaches to improve the ability of crops to withstand and produce higher yields in response to changing climate circumstances. This review paper examines the incorporation of artificial intelligence (AI) into conventional wheat breeding methods, with a focus on the contribution of AI in tackling the intricacies of contemporary agriculture. This review aims to assess the influence of AI technologies on enhancing the efficiency, precision, and sustainability of wheat breeding projects. We conduct a thorough analysis of recent research to evaluate several applications of artificial intelligence, such as machine learning (ML), deep learning (DL), and genomic selection (GS). These technologies expedite the swift analysis and interpretation of extensive datasets, augmenting the process of selecting and breeding wheat varieties that are well-suited to a wide range of environmental circumstances. The findings from the examined research demonstrate notable progress in wheat breeding as a result of artificial intelligence. ML algorithms have enhanced the precision of predicting phenotypic traits, whereas genomic selection has reduced the duration of breeding cycles. Utilizing artificial intelligence, high-throughput phenotyping allows for meticulous examination of plant characteristics under different stress environments, facilitating the identification of robust varieties. Furthermore, AI-driven models have exhibited superior predicted accuracies for crop productivity and disease resistance in comparison to conventional methods. AI technologies play a crucial role in the modernization of wheat breeding, providing significant enhancements in crop performance and adaptability. This integration not only facilitates the growth of wheat cultivars that provide large yields and can withstand stressful conditions but also strengthens global food security in the context of climate change. Ongoing study and collaboration across several fields are crucial to improving and optimizing these AI applications, ultimately enhancing their influence on sustainable agriculture.

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