ArticleThe Plant cell2020
ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States.
Article in The Plant cell, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 36 citations in OpenAlex.
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- APTES: a high-throughput deep learning-based Arabidopsis phenotypic trait estimation system for individual leaves and siliques.aBIOTECH · 2025Article
- EcoBOT: an AI/ML enabled automated phenotyping capability for model plants.Frontiers in plant science · 2025Article
- Insights into the function of the chloroplastic ribosome-associated GTPase high frequency of lysogenization X inPlant direct · 2024Article
- Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation.Nature communications · 2023Article
- Estimation of rice seedling growth traits with an end-to-end multi-objective deep learning framework.Frontiers in plant science · 2023Article
- Research of intelligent reasoning system ofFrontiers in plant science · 2023Article
- Predicting Gene Regulatory Interactions Using Natural Genetic Variation.Methods in molecular biology (Clifton, N.J.) · 2023Article
- HeliantHOME, a public and centralized database of phenotypic sunflower data.Scientific data · 2022Article
- Systematic histone H4 replacement in Arabidopsis thaliana reveals a role for H4R17 in regulating flowering time.The Plant cell · 2022Article
- Attention-optimized DeepLab V3 + for automatic estimation of cucumber disease severity.Plant methods · 2022Article
- Development of an Image Analysis Pipeline to Estimate Sphagnum Colony Density in the Field.Plants (Basel, Switzerland) · 2021Article
- Got Rosettes? Phenotype Them Fast, Accurately, and Easily with ARADEEPOPSIS!The Plant cell · 2020Article
Corrections and comments
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- Erratum issued
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.