The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants

Daniel Buchvaldt Amby; Jesper Cairo Westergaard; Dominik K. Grosskinsky; Signe Marie Jensen; Jesper Svensgaard; Fulai Liu; Svend Christensen; Thomas Roitsch

doi:10.1016/j.atech.2025.100845

The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants

Daniel Buchvaldt Amby^*, Jesper Cairo Westergaard, Dominik K. Grosskinsky, Signe Marie Jensen, Jesper Svensgaard, Fulai Liu, Svend Christensen, Thomas Roitsch

^*Corresponding author for this work

Department of Agroecology - Crop Health

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

1 Citation (Scopus)

Abstract

Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g., multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.

Original language	English
Article number	100845
Journal	Smart Agricultural Technology
Volume	11
ISSN	2772-3755
DOIs	https://doi.org/10.1016/j.atech.2025.100845
Publication status	E-pub / Early view - 21 Feb 2025

Keywords

Abiotic and biotic stress
Drought
Multispectral imaging systems
Multispectral signatures
Plant disease
Plant phenomics
Robotic systems

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Amby, D. B., Westergaard, J. C., Grosskinsky, D. K., Jensen, S. M., Svensgaard, J., Liu, F., Christensen, S., & Roitsch, T. (2025). The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants. Smart Agricultural Technology, 11, Article 100845. Advance online publication. https://doi.org/10.1016/j.atech.2025.100845

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title = "The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants",

abstract = "Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g., multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.",

keywords = "Abiotic and biotic stress, Drought, Multispectral imaging systems, Multispectral signatures, Plant disease, Plant phenomics, Robotic systems",

author = "Amby, {Daniel Buchvaldt} and Westergaard, {Jesper Cairo} and Grosskinsky, {Dominik K.} and Jensen, {Signe Marie} and Jesper Svensgaard and Fulai Liu and Svend Christensen and Thomas Roitsch",

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doi = "10.1016/j.atech.2025.100845",

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Amby, DB, Westergaard, JC, Grosskinsky, DK, Jensen, SM, Svensgaard, J, Liu, F, Christensen, S & Roitsch, T 2025, 'The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants', Smart Agricultural Technology, vol. 11, 100845. https://doi.org/10.1016/j.atech.2025.100845

The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants. / Amby, Daniel Buchvaldt; Westergaard, Jesper Cairo; Grosskinsky, Dominik K. et al.
In: Smart Agricultural Technology, Vol. 11, 100845, 08.2025.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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AU - Amby, Daniel Buchvaldt

AU - Westergaard, Jesper Cairo

AU - Grosskinsky, Dominik K.

AU - Jensen, Signe Marie

AU - Svensgaard, Jesper

AU - Liu, Fulai

AU - Christensen, Svend

AU - Roitsch, Thomas

PY - 2025/2/21

Y1 - 2025/2/21

N2 - Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g., multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.

AB - Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called “PhenoLab”. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365–970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g., multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.

KW - Abiotic and biotic stress

KW - Drought

KW - Multispectral imaging systems

KW - Multispectral signatures

KW - Plant disease

KW - Plant phenomics

KW - Robotic systems

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DO - 10.1016/j.atech.2025.100845

M3 - Journal article

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JO - Smart Agricultural Technology

JF - Smart Agricultural Technology

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ER -

The PhenoLab – an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants

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Keywords

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