By combining visible, near-infrared, thermal, and chlorophyll fluorescence imaging, the system captures key indicators such as leaf temperature, photosynthetic efficiency, and compactness without damaging plants. Tested on lettuce and Arabidopsis under drought, salt, and UV-B conditions, MADI uncovered early-warning markers and novel stress responses, including chlorophyll hormesis.
Global agriculture is under mounting pressure from climate change, population growth, and declining nutrient content in food crops. Developing tools that can rapidly and accurately monitor plant health is essential to maintain productivity and sustainability. While genetic sequencing has advanced, the ability to connect genetic variation with actual plant traits remains a bottleneck. High-throughput phenotyping (HTP) has emerged to bridge this gap by providing non-invasive monitoring of growth and physiology. Existing platforms—whether field-based robots or controlled-environment systems—offer various imaging techniques, but many struggle to integrate multiple parameters at once. Against this backdrop, new solutions like MADI aim to provide a holistic view of plant development and stress responses.
A study (DOI: 10.1016/j.plaphe.2025.100040) published in Plant Phenomics on 31 March 2025 by Dominique Van Der Straeten’s team, Ghent University, provides a powerful tool for researchers and agricultural practitioners aiming to improve crop resilience in the face of climate change.
The MADI system was developed as a high-throughput phenotyping system combining multiple imaging modalities on a robotized platform. It integrates two cameras, one for thermal imaging in the long-wave infrared region and another for visible and near-infrared reflectance, fitted with interchangeable filters and an enclosed imaging box to minimize ambient light. This setup allows sequential capture of RGB, NIR, fluorescence, and thermal images, with a focus on seedlings and horizontally growing species such as lettuce and Arabidopsis. Controlled remotely via custom software, the workflow encompasses image acquisition, preprocessing, segmentation, and parameter analysis, producing quantitative traits such as rosette area, diameter, compactness, chlorophyll fluorescence, and leaf temperature. Using a fluorescence-based ratio (F730/F700), MADI provides a non-destructive method for estimating chlorophyll content, validated across Arabidopsis, tomato, and lettuce, though saturation at high pigment levels indicates species-specific calibration is needed. The system’s performance was demonstrated in multiple stress assays. Under drought, lettuce plants exhibited early increases in leaf temperature before visible wilting, along with reduced compactness and disrupted diurnal rhythms, marking these parameters as sensitive drought indicators. Salt stress experiments with Arabidopsis revealed an unexpected hormetic response, where mild salinity initially increased chlorophyll content before a decline under prolonged or severe conditions—a phenomenon absent in ethylene-insensitive mutants. Further tests of hormonal pathway mutants and ecotypes confirmed that compactness reliably distinguished differences in growth regulation and stress sensitivity. Finally, exposure to UV-B light reduced photosynthetic efficiency in Arabidopsis, with mutants lacking the UVR8 photoreceptor showing stronger declines than wild type, highlighting the system’s ability to capture genetic differences in stress adaptation. Together, these results establish MADI as a versatile, non-invasive platform for monitoring plant growth and physiology under environmental stress.
MADI provides researchers and breeders with a powerful non-destructive tool for monitoring plant health over time. Its ability to integrate multiple imaging modes means that growth, temperature, photosynthetic efficiency, and chlorophyll content can be assessed simultaneously. For agriculture, the system offers early-warning indicators of stress that could guide irrigation, nutrient management, or crop selection. For genetics and breeding, MADI enables large-scale screening of natural populations and mutant libraries to identify lines with desirable resilience traits.
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References
DOI
10.1016/j.plaphe.2025.100040
Original URL
https://doi.org/10.1016/j.plaphe.2025.100040
Funding information
DVDS gratefully acknowledges funding by the EU's Research and Innovation program Horizon 2020 (TimeScale), the Research Foundation Flanders (FWO, project G032717N), the Francqui Foundation for the Collen-Francqui Research Professorship (STI.DIV.2022.0014.01) awarded to her, and Ghent University.
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.