Multi-scale advanced approaches to high-throughput phenotyping in crop improvement

Abstract

The advent of high-throughput phenotyping (HTP) technologies has revolutionized crop improvement by enabling rapid, non-destructive measurement of multiple plant traits. These advanced methods facilitate the efficient collection of phenotypic data, bridging the gap between traditional phenotyping and modern genomics. These technologies allow for the comprehensive analysis of complex traits, such as growth, yield and stress adaptations, under diverse environmental conditions. By integrating imaging techniques like near infrared, far infrared, thermal and hyper spectral imaging techniques with machine learning algorithms, high throughput phenotyping enhances the accuracy and efficiency of plant characters measurements. This dynamic approach enables the discovery of novel traits and accelerates breeding programs by providing deeper insights into genotype-phenotype relationships. Additionally, these technologies supports the continuous monitoring of plant development, stress responses and adaptive mechanisms, offering a more general perception of plant-environment interactions. The incorporation of robotics and automation in this technology not only increases precision but also allows for repeated, non-invasive measurements, fostering more informed breeding decisions. As these new technologies continue to advance, they hold the capacity to significantly accelerate the development of improved crop varieties, addressing the challenges of modern agriculture.

Plant phenotyping relevance

作物改良におけるハイスループット植物表現型解析技術を中心に、画像・センサー・機械学習・ロボティクスによる形質測定を総説しているため。

Code and data availability

The supplied blocks contain only the conclusion and reference list of a narrative review on high-throughput phenotyping. No phenotype datasets, images, author code, models, or supplements tied to this paper's own measurements or analysis are mentioned; all URLs are citations to prior work.

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