High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges

文献类型： 外文期刊

第一作者： Cheng, Tao

作者： Cheng, Tao;Zhang, Dongyan;Cheng, Tao;Wang, Zhaoming;Zhang, Dongyan;Zhang, Gan;Yuan, Feng;Liu, Yaling;Wang, Tianyi;Ren, Weibo;Zhao, Chunjiang

作者机构：

关键词： Forage; High-throughput phenotyping; Precision identification; Sensors; Artificial intelligence; Efficient breeding

期刊名称：ARTIFICIAL INTELLIGENCE IN AGRICULTURE （ 影响因子：12.4； 五年影响因子：12.7 ）

ISSN： 2097-2113

年卷期： 2025 年 15 卷 1 期

页码：

收录情况： SCI

摘要： 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. (c) 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序