Reports from South Dakota State University Describe Recent Advances in Artificia l Intelligence (Integrating Genomics, Phenomics, and Deep Learning Improves the Predictive Ability for Fusarium Head Blight-related Traits In Winter Wheat)

南达科他州立大学的报告描述了人工智能的最新进展(整合基因组学、表型组学和深度学习提高了冬小麦镰刀菌穗病相关性状的预测能力)

Robotics & Machine Learning Daily News2024，Issue(Jul.3) ：110-111.

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Reports from South Dakota State University Describe Recent Advances in Artificia l Intelligence (Integrating Genomics, Phenomics, and Deep Learning Improves the Predictive Ability for Fusarium Head Blight-related Traits In Winter Wheat)

南达科他州立大学的报告描述了人工智能的最新进展(整合基因组学、表型组学和深度学习提高了冬小麦镰刀菌穗病相关性状的预测能力)

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摘要

一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-在一份新的报告中讨论了人工智能的研究结果。根据NewsRx编辑在南达科他州Brookin GS的新闻报道，研究表明，“镰刀菌头疫病(FHB)仍然是小麦(Triticum aestivum L .)最具破坏性的病害之一，造成产量和最终用途品质的巨大损失。FHB抗性性状、镰刀菌损伤籽粒(FDK)和脱氧雪腐镰刀菌烯醇(DON)的表型分型容易受到人为偏见或资源昂贵的影响。阻碍了抗FHB品种选育的进展。这项研究的资金支持者包括农业研究服务社、国家粮食和农业研究所。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Artificial Intell igence are discussed in a new report. According to news reporting out of Brookin gs, South Dakota, by NewsRx editors, research stated, “Fusarium head blight (FHB ) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB res istance traits, Fusarium-damaged kernels (FDK), and deoxynivalenol (DON), is eit her prone to human biases or resource expensive, hindering the progress in breed ing for FHB-resistant cultivars.” Financial supporters for this research include Agricultural Research Service, Na tional Institute of Food and Agriculture.

Key words

Brookings/South Dakota/United States/North and Central America/Artificial Intelligence/Emerging Technologies/Genet ics/Machine Learning/South Dakota State University

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出版年

2024

Robotics & Machine Learning Daily News

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