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

Abstract

• 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 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.

Plant phenotyping relevance

植物の画像取得・解析、ストレス定量、ソフトウェアを統合した高スループット表現型解析プラットフォームの開発が中心である。

Code and data availability

The paper's PhenoLab phenotype measurements (multispectral/fluorescence imaging of barley powdery mildew and maize drought, image analysis statistics, segmentation) are not publicly deposited; the authors state data are available only on request. No public code, model, or dataset URL is provided.

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