ArticlePlant methods2015
Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis.
Article in Plant methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- Detached Twig Assay to Evaluate Bacterial Canker on Peaches.Methods and protocols · 2026Article
- Transcriptome Analysis of Sweet Cherry (Plants (Basel, Switzerland) · 2023Article
- Detection, Diagnosis, and Preventive Management of the Bacterial Plant PathogenPlants (Basel, Switzerland) · 2023Review
- L2MXception: an improved Xception network for classification of peach diseases.Plant methods · 2021Article
- Phage biocontrol to combat Pseudomonas syringae pathogens causing disease in cherry.Microbial biotechnology · 2020Article
- Genome-wide association multi-locus and multi-variate linear mixed models reveal two linked loci with major effects on partial resistance of apricot to bacterial canker.BMC plant biology · 2019Article
- Digital Imaging Combined with Genome-Wide Association Mapping Links Loci to Plant-Pathogen Interaction Traits.Plant physiology · 2018Article
- Characterization of the pathogenicity of strains ofPlant pathology · 2018Article
- An integrated RNAseq-BMC plant biology · 2017Article
- Biochar Amendment Modifies Expression of Soybean andFrontiers in plant science · 2017Article
- Quantitative, Image-Based Phenotyping Methods Provide Insight into Spatial and Temporal Dimensions of Plant Disease.Plant physiology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance.
resultsIn this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study.
conclusionsAutomated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.
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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.