ArticlePlant phenomics (Washington, D.C.)2020
"Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
20 citing papers in PubMed.
- Unlocking the potential of spring wheat genetic resources: uncovering resistance sources against leaf rust and yellow rust.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- GBS-based identification ofFrontiers in plant science · 2026Article
- Foliar disease resistance phenomics of fungal pathogens: image-based approaches for mapping quantitative resistance in cereal germplasm.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Comparative analysis of distinct phenotyping methods for assessing wheat resistance and pathogen virulence among Fusarium species causing head blight disease.Plant methods · 2025Article
- Deep phenotyping platform for microscopic plant-pathogen interactions.Frontiers in plant science · 2025Article
- Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines.Current issues in molecular biology · 2024Article
- PlanText: Gradually Masked Guidance to Align Image Phenotypes with Trait Descriptions for Plant Disease Texts.Plant phenomics (Washington, D.C.) · 2024Article
- Broad-spectrum resistance to fungal foliar diseases in wheat: recent efforts and achievements.Frontiers in plant science · 2024Review
- StripeRust-Pocket: A Mobile-Based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust.Plant phenomics (Washington, D.C.) · 2024Article
- AISOA-SSformer: An Effective Image Segmentation Method for Rice Leaf Disease Based on the Transformer Architecture.Plant phenomics (Washington, D.C.) · 2024Article
- A Multi-Modal Open Object Detection Model for Tomato Leaf Diseases with Strong Generalization Performance Using PDC-VLD.Plant phenomics (Washington, D.C.) · 2024Article
- Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning.Plant phenomics (Washington, D.C.) · 2023Article
- Diversifying Resistance Mechanisms in Cereal Crops Using Microphenomics.Plant phenomics (Washington, D.C.) · 2023Review
- Article
- Sensor-based phenotyping of above-ground plant-pathogen interactions.Plant methods · 2022Review
- Mining for New Sources of Resistance to Powdery Mildew in Genetic Resources of Winter Wheat.Frontiers in plant science · 2022Article
- High throughput saliency-based quantification of grape powdery mildew at the microscopic level for disease resistance breeding.Horticulture research · 2022Article
- Opportunities and limits of controlled-environment plant phenotyping for climate response traits.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Review
- Efficiency of a Seedling Phenotyping Strategy to Support European Wheat Breeding Focusing on Leaf Rust Resistance.Biology · 2021Article
- Quantitative Trait Loci Mapping of Adult Plant and Seedling Resistance to Stripe Rust (Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a "Macrophenomics facility." The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.
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Registered trials
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.