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

作者：Daniel Buchvaldt Amby, Jesper Cairo Westergaard, Dominik K. Großkinsky, Signe Marie Jensen, Jesper Svensgaard, Fulai Liu, Svend Christensen, Thomas Roitsch · 发表于：Smart Agricultural Technology · 年份：2025 · DOI：10.1016/j.atech.2025.100845 · 被引用次数：6 · 研究领域：Plant-Microbe Interactions and Immunity、Leaf Properties and Growth Measurement、Genetic Mapping and Diversity in Plants and Animals

• A high-precision digital platform for plant phenotyping and cultivation was developed. • LED illumination based multispectral imaging system to study crop plant responses. • Different multispectral signatures were induced by plant abiotic and biotic factors. 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...