饲料高通量表型技术:现状、瓶颈和挑战

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Tao Cheng , Dongyan Zhang , Gan Zhang , Tianyi Wang , Weibo Ren , Feng Yuan , Yaling Liu , Zhaoming Wang , Chunjiang Zhao
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引用次数: 0

摘要

高通量表型(HTP)技术是目前牧草优良遗传资源高效选择和育种的重要瓶颈。为了更好地了解牧草表型研究的现状和确定重点发展方向,本文对近十年来HTP技术用于牧草表型分析的进展进行了综述。本文综述了牧草表型监测的独特方面和研究重点,重点介绍了关键的遥感平台,研究了先进的传感技术在表型性状量化方面的应用,探讨了人工智能(AI)算法在表型数据集成和分析方面的应用,并评估了表型基因组学的最新进展。HTP技术在饲料中的实际应用仍然受到一些挑战的制约。其中包括建立统一的数据收集标准,设计有效的算法来处理复杂的遗传和环境相互作用,深化表型学与基因组学的交叉探索,解决饲料表型生长监测模型的病理反转问题,以及开发低成本的饲料表型设备。解决这些挑战将释放HTP的全部潜力,能够精确识别优质饲料性状,加速优良品种的育种,最终提高饲料产量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges
High-throughput phenotyping (HTP) technology is now a significant bottleneck in the efficient selection and breeding of superior forage genetic resources. To better understand the status of forage phenotyping research and identify key directions for development, this review summarizes advances in HTP technology for forage phenotypic analysis over the past ten years. This paper reviews the unique aspects and research priorities in forage phenotypic monitoring, highlights key remote sensing platforms, examines the applications of advanced sensing technology for quantifying phenotypic traits, explores artificial intelligence (AI) algorithms in phenotypic data integration and analysis, and assesses recent progress in phenotypic genomics. The practical applications of HTP technology in forage remain constrained by several challenges. These include establishing uniform data collection standards, designing effective algorithms to handle complex genetic and environmental interactions, deepening the cross-exploration of phenomics-genomics, solving the problem of pathological inversion of forage phenotypic growth monitoring models, and developing low-cost forage phenotypic equipment. Resolving these challenges will unlock the full potential of HTP, enabling precise identification of superior forage traits, accelerating the breeding of superior varieties, and ultimately improving forage yield.
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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