Data management challenges for artificial intelligence in plant and agricultural research.
Data management challenges for artificial intelligence in plant and agricultural research.
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DOI：
10.12688/f1000research.52204.2
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发表时间：
2021
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人工智能(AI)在植物科学中的应用越来越多，但它远未在该领域得到常规和有效的实施。与开发新的粮食和农业技术特别相关的是开发有效的、有意义的和可用的方法，以整合、比较和可视化来自不同来源和科学方法的大型多维数据集。在简要总结了植物科学中对数据科学和人工智能感兴趣的原因后，本文确定并讨论了数据管理中的八个关键挑战，这些挑战必须得到解决，以进一步释放人工智能在作物和农艺研究中的潜力，特别是机器学习(AI)的应用，它在该领域具有很大的前景。
Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.