Bhuiyan Mohammad Muzahidur Rahman, Noman Inshad Rahman, Aziz Md Munna, Rahaman Md Mizanur, Islam Md Rashedul, Manik Mia Md Tofayel Gonee, Das Kallol
College of Business, Westcliff University, Irvine, CA 92614, USA.
Department of Computer Science, California State University, Los Angeles, CA 90032, USA.
Front Biosci (Elite Ed). 2025 Jan 23;17(1):27936. doi: 10.31083/FBE27936.
Our study focused on plant breeding, from traditional methods to the present most advanced genetic and data-driven concepts. Conventional breeding techniques, such as mass selection and cross-breeding, have been instrumental in crop improvement, although they possess inherent limitations in precision and efficiency. Advanced molecular methods allow breeders to improve crops quicker by more accurately targeting specific traits. Data analytics and information technology (IT) are crucial in modern plant breeding, providing tools for data management, analysis, and interpretation of large volumes of data from genomic, phenotypic, and environmental sources. Meanwhile, emerging technologies in machine learning, high-throughput phenotyping, and the Internet of Things (IoT) provide real-time insights into the performance and responses of plants to environmental variables, enabling precision breeding. These tools will allow breeders to select complex traits related to yield, disease resistance, and abiotic stress tolerance more precisely and effectively. Moreover, this data-driven approach will enable breeders to use resources judiciously and make crops resilient, thus contributing to sustainable agriculture. Data analytics integrated into IT will enhance traditional breeding and other key applications in sustainable agriculture, such as crop yield improvement, biofortification, and climate change adaptation. This review aims to highlight the role of interdisciplinary collaboration among breeders, data scientists, and agronomists in absorbing these technologies. Further, this review discusses the future trends that will make plant breeding even more effective with this new wave of artificial intelligence (AI), blockchain, and collaborative platforms, bringing new data transparency, collaboration, and predictability levels. Data and IT-based breeding will greatly contribute to future global food security and sustainable food production. Thus, creating high-performing, resource-efficient crops will be the foundation of a future agricultural vision that balances environmental care. More technological integration in plant breeding is needed for resilient and sustainable food systems to handle the growing population and changing climate challenges.
我们的研究聚焦于植物育种,涵盖从传统方法到当前最先进的基因和数据驱动理念。传统育种技术,如混合选择和杂交育种,在作物改良中发挥了重要作用,尽管它们在精准度和效率方面存在固有局限性。先进的分子方法使育种者能够通过更精确地靶向特定性状来更快地改良作物。数据分析和信息技术在现代植物育种中至关重要,为来自基因组、表型和环境数据源的大量数据的管理、分析和解读提供工具。同时,机器学习、高通量表型分析和物联网等新兴技术能实时洞察植物对环境变量的表现和反应,实现精准育种。这些工具将使育种者能够更精确、有效地选择与产量、抗病性和非生物胁迫耐受性相关的复杂性状。此外,这种数据驱动的方法将使育种者明智地利用资源并培育出具有韧性的作物,从而推动可持续农业发展。融入信息技术的数据分析将加强传统育种以及可持续农业中的其他关键应用,如提高作物产量、生物强化和适应气候变化。本综述旨在强调育种者、数据科学家和农学家之间跨学科合作在采用这些技术方面的作用。此外,本综述还讨论了未来趋势,即借助人工智能、区块链和协作平台的新一波浪潮使植物育种更有效,带来新的数据透明度、协作和可预测水平。基于数据和信息技术的育种将极大地促进未来全球粮食安全和可持续粮食生产。因此,培育高性能、资源高效型作物将是平衡环境保护的未来农业愿景的基础。为了建立有韧性和可持续的粮食系统以应对不断增长的人口和气候变化挑战,植物育种需要更多的技术整合。