High-throughput Screening and Trait Dissection for Seed Quality Enhancement

Abstract

High-throughput phenotyping (HTP) has transformed seed testing, quality evaluation, storage, and stress response assessment by enabling rapid, non-destructive, and high-resolution analysis of seed traits. Traditional seed evaluation methods are labour-intensive and time-consuming, whereas HTP employs advanced imaging, sensor technologies, and machine learning algorithms to assess seed morphology, physiological traits, and biochemical properties efficiently. In seed testing, HTP accelerates germination studies, vigour assessments, and stress tolerance evaluations, facilitating the identification of high-quality and resilient seed varieties. It also enhances seed storage practices by providing real-time monitoring of seed viability, detecting deterioration factors, and optimizing storage conditions. Furthermore, HTP significantly contributes to understanding seed responses to biotic and abiotic stresses. By characterizing genetic and physiological factors associated with disease resistance and environmental stress tolerance, HTP aids in breeding stress-resilient crops and optimizing seed treatments. The integration of HTP with artificial intelligence further refines predictive modelling and precision agriculture strategies, supporting climate-resilient farming and sustainable agricultural practices. This paper highlights the multifaceted role of HTP in advancing seed science, from quality assurance to stress management, underscoring its impact on agricultural productivity and genetic resource conservation.

Plant phenotyping relevance

種子形質の取得に用いる高スループット画像・センサー・機械学習手法を中心に概説するレビューであり、植物フェノタイピング手法の方法論的役割が明確。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.