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Beyond high‐throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process‐based models.

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Title: Beyond high‐throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process‐based models.
Authors: Ting, To‐Chia1 (AUTHOR), Mackay, D. Scott2 (AUTHOR), Jung, Jinha3 (AUTHOR), Reynolds, Matthew P.4 (AUTHOR), Yang, Yang5 (AUTHOR), Wang, Diane R.1 (AUTHOR) drwang@purdue.edu
Source: New Phytologist. May2026, Vol. 250 Issue 3, p1468-1482. 15p.
Subjects: Physiological models, Phenotypes, Detectors, Crop growth, Plant physiology, Plant phenology
Abstract: Summary: The last decade has marked a period of rapid innovation in high‐throughput phenotyping (HTP) of plants. This includes the establishment of robotic phenotyping infrastructure, development of new sensors, and improvements in computation for downstream analysis. While HTP approaches have revolutionized data collection, meaningful insights into plant function require a yet deeper connection between resultant HTP‐based information and biological responses. We suggest that dynamic process‐based plant models, which simulate growth and physiology in a time‐explicit manner, can serve as a functional link between high‐throughput methods and whole‐plant mechanisms of growth. Using this framework, we review recent research that has leveraged HTP approaches for estimation of plant traits that are commonly used as process‐based model (PBM) variables. Through this analysis, we review successes and identify emerging directions for future research. Finally, we highlight the varied ways that HTP can be used in conjunction with PBMs as a tool to advance discovery and improve prediction of plant growth. [ABSTRACT FROM AUTHOR]
Copyright of New Phytologist is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192872587
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Beyond high‐throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process‐based models.
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  Data: <searchLink fieldCode="DE" term="%22Physiological+models%22">Physiological models</searchLink><br /><searchLink fieldCode="DE" term="%22Phenotypes%22">Phenotypes</searchLink><br /><searchLink fieldCode="DE" term="%22Detectors%22">Detectors</searchLink><br /><searchLink fieldCode="DE" term="%22Crop+growth%22">Crop growth</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+physiology%22">Plant physiology</searchLink><br /><searchLink fieldCode="DE" term="%22Plant+phenology%22">Plant phenology</searchLink>
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  Data: Summary: The last decade has marked a period of rapid innovation in high‐throughput phenotyping (HTP) of plants. This includes the establishment of robotic phenotyping infrastructure, development of new sensors, and improvements in computation for downstream analysis. While HTP approaches have revolutionized data collection, meaningful insights into plant function require a yet deeper connection between resultant HTP‐based information and biological responses. We suggest that dynamic process‐based plant models, which simulate growth and physiology in a time‐explicit manner, can serve as a functional link between high‐throughput methods and whole‐plant mechanisms of growth. Using this framework, we review recent research that has leveraged HTP approaches for estimation of plant traits that are commonly used as process‐based model (PBM) variables. Through this analysis, we review successes and identify emerging directions for future research. Finally, we highlight the varied ways that HTP can be used in conjunction with PBMs as a tool to advance discovery and improve prediction of plant growth. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of New Phytologist is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/nph.71039
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      – Code: eng
        Text: English
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      – SubjectFull: Physiological models
        Type: general
      – SubjectFull: Phenotypes
        Type: general
      – SubjectFull: Detectors
        Type: general
      – SubjectFull: Crop growth
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      – SubjectFull: Plant physiology
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      – SubjectFull: Plant phenology
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      – TitleFull: Beyond high‐throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process‐based models.
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              Text: May2026
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              Y: 2026
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