Roots are central to water and nutrient uptake, anchorage, and soil microorganism interactions, yet they remain difficult to measure in the field. Traditional root phenotyping often relies on destructive digging, soil coring, or minirhizotrons that capture only part of the root system. Controlled-environment rhizotrons allow repeated imaging, but most soil-based platforms have limited capacity, require manual handling, or cannot phenotype roots and shoots together at scale. These constraints slow genetic studies and breeding pipelines that need hundreds of plants. Given these challenges, in-depth research is needed to develop automated, standardized, high-throughput platforms that can monitor whole-plant growth in soil over time.
Researchers at the Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Germany, published (DOI: 10.1016/j.plaphe.2026.100213) the study in Plant Phenomics (2026; volume 8). They describe GrowScreen-Rhizo 3, a modular plant-to-sensor platform with 896 rhizotrons, five automated guided vehicles (AGVs), four measurement chambers, and a semi-automated filling line. The system images roots at 116 µm per pixel and shoots from six angles, while weighing and watering each rhizotron individually to track growth and water use.
The platform uses large rhizotrons measuring 80 × 40 × 5 cm, filled with soil and tilted at 45° with a transparent side facing downward. AGVs lift tables and transport them to measurement chambers, where individual rhizotrons are pulled inside for daily imaging, weighing, and irrigation. Roots are imaged at high resolution, and shoots are captured from top, side, and 45° views; artificial neural networks segment shoot images to predict leaf area and height. The team tested whether daily movement and imaging affected plant growth. In sunflower (Helianthus annuus), wheat (Triticum aestivum), and maize (Zea mays), moved and imaged plants showed no significant differences in root dry weight, shoot dry weight, root-to-shoot ratio, leaf area, planar root length, or root convex hull area compared with unmoved controls. The platform also supported barley (Hordeum vulgare), rapeseed (Brassica napus), lentil (Lens culinaris), buckwheat (Fagopyrum esculentum), and evening primrose (Oenothera biennis). In a 192-rhizotron barley experiment with 24 genotypes, image-based predicted leaf area and planar root length tracked growth over time. Heritability reached 0.52 for root system depth and 0.93 for seminal root length. Planar root length correlated strongly with scanned root length and root dry weight, and principal component analysis separated genotypes using image-based traits alone.
The authors said the platform addresses a practical bottleneck: roots are essential for crop performance, but they are hidden, difficult to handle, and rarely measured at breeding scale. They said GrowScreen-Rhizo 3 was built to move hundreds of plants gently, image them without disturbing growth, and combine root, shoot, and water-use data in one workflow. According to the authors, automated handling did not compromise plant development, and image-based root traits revealed genetic variation that destructive sampling might miss. They said the modular design can be scaled up or adapted for different species, treatments, and experimental questions.
GrowScreen-Rhizo 3 could support genome-wide association studies (GWAS), quantitative trait locus (QTL) mapping, and pre-breeding by phenotyping hundreds of genotypes under controlled conditions. Its water-use measurements and adjustable irrigation can simulate drought or nutrient treatments, helping identify plants with improved resource use efficiency. The platform may also aid studies of root-soil interactions and rhizosphere processes, although high-throughput microbiome and root exudate sampling remain challenging. By linking early root traits to shoot biomass and yield, the system could help bridge greenhouse phenotyping and field trials. Access is available through national and international phenotyping networks, including the German Plant Phenotyping Network (DPPN).
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Reference
DOI
10.1016/j.plaphe.2026.100213
Original Source URL
https://doi.org/10.1016/j.plaphe.2026.100213
Funding information
German Federal Ministry of Education and Research (BMBF) German Plant Phenotyping Network (DPPN), Fkz. 031A053; European Union CROPINNO grant agreement No. 01059784; BMBF DPPN-Access Fkz. 031B121; MAZE3 Fkz. 031B1301E; InnoWert Fkz. 03WIR3024B; CSC scholarship No. 202107720055.
About Plant Phenomics
Plant Phenomics publishes breakthrough research that integrates genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics and computer science to advance plant phenotyping—from the cellular level to entire populations. The journal tackles key scientific challenges in phenomics by showcasing cutting‑edge technologies for data acquisition, management, interpretation and modelling. Its scope spans practical applications across all plant sciences, aiming to translate novel phenotyping tools into real‑world solutions for breeding, crop management and fundamental plant biology. By fostering interdisciplinary innovation, Plant Phenomics serves as a hub for researchers developing high‑throughput, accurate and scalable methods to decipher complex plant traits, ultimately accelerating progress in agriculture and plant science.