ArticleFrontiers in plant science2019
High-Throughput Phenotyping of Fire Blight Disease Symptoms Using Sensing Techniques in Apple.
Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed, 39 citations in OpenAlex.
- Design and Evaluation of Synthetic Microbial Communities for Effective Fire Blight Disease Control in Apples.Journal of microbiology and biotechnology · 2025Article
- Characterization of Hazelnut Trees in Open Field Through High-Resolution UAV-Based Imagery and Vegetation Indices.Sensors (Basel, Switzerland) · 2025Article
- Annotated image dataset of fire blight symptoms for object detection in orchards.Data in brief · 2024Article
- An Evaluation of Multi-Channel Sensors and Density Estimation Learning for Detecting Fire Blight Disease in Pear Orchards.Sensors (Basel, Switzerland) · 2024Article
- An app for apples: Citizen-led mapping of fire blight in Central Asia.Journal of plant pathology : an international journal of the Italian Phytopathological Society · 2024Article
- Image-Based High-Throughput Phenotyping in Horticultural Crops.Plants (Basel, Switzerland) · 2023Review
- Article
- A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Washington State produces about 70% of total fresh market apples in the United States. One of the primary goals of apple breeding programs is the development of new cultivars resistant to devastating diseases such as fire blight. The overall objective of this study was to investigate high-throughput phenotyping techniques to evaluate fire blight disease symptoms in apple trees. In this regard, normalized stomatal conductance data acquired using a portable photosynthetic system, image data collected using RGB and multispectral cameras, and visible-near infrared spectral reflectance acquired using a hyperspectral sensing system, were independently evaluated to estimate the progression of fire blight infection in young apple trees. Sensors with ranging complexity - from simple RGB to multispectral imaging to hyperspectral system - were evaluated to select the most accurate technique for the assessment of fire blight disease symptoms. The proximal multispectral images and visible-near infrared spectral reflectance data were collected in two field seasons (2016, 2017); while, proximal side-view RGB images and multispectral images using unmanned aerial systems were collected in 2017. The normalized stomatal conductance data was correlated with disease severity rating (
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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.