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

Abstract

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. • A systematic review of the application status of HTP technology in forage phenotypic analysis. • The uniqueness and research focus of forage phenotypic monitoring were introduced. • The specific application of advanced sensing technologies and AI algorithms in forage phenotypic data integration and feature quantification. • The research progress of forage phenomenics-genomics was reviewed. • The challenges and future directions of HTP technology in the application of forage were discussed.

Plant phenotyping relevance

飼料作物の高スループット表現型解析について、センシング、リモートセンシング、AIによる形質定量化を中心に扱う方法論レビューである。

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