Citizen crowds and experts: observer variability in image-based plant phenotyping
Published 2018 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Citizen crowds and experts: observer variability in image-based plant phenotyping
Authors
Keywords
Phenotyping, Image-based, Observer, Agreement, Variability, Crowdsourcing, Citizen-science
Journal
Plant Methods
Volume 14, Issue 1, Pages -
Publisher
Springer Nature
Online
2018-02-09
DOI
10.1186/s13007-018-0278-7
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants
- (2017) Massimo Minervini et al. PLANT JOURNAL
- A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
- (2017) Hsiang Sing Naik et al. Plant Methods
- Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area
- (2016) Nan An et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Evaluating the level of agreement between human and time-lapse camera observations of understory plant phenology at multiple scales
- (2016) David N. Laskin et al. Ecological Informatics
- Finely-grained annotated datasets for image-based plant phenotyping
- (2016) Massimo Minervini et al. PATTERN RECOGNITION LETTERS
- 3-D Imaging Systems for Agricultural Applications—A Review
- (2016) Manuel Vázquez-Arellano et al. SENSORS
- Machine Learning for Plant Phenotyping Needs Image Processing
- (2016) Sotirios A. Tsaftaris et al. TRENDS IN PLANT SCIENCE
- Machine Learning for High-Throughput Stress Phenotyping in Plants
- (2016) Arti Singh et al. TRENDS IN PLANT SCIENCE
- Lights, camera, action: high-throughput plant phenotyping is ready for a close-up
- (2015) Noah Fahlgren et al. CURRENT OPINION IN PLANT BIOLOGY
- Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner]
- (2015) Massimo Minervini et al. IEEE SIGNAL PROCESSING MAGAZINE
- Growth Signatures of Rosette Plants from Time-Lapse Video
- (2015) Babette Dellen et al. IEEE-ACM Transactions on Computational Biology and Bioinformatics
- Multi-modality imagery database for plant phenotyping
- (2015) Jeffrey A. Cruz et al. MACHINE VISION AND APPLICATIONS
- Leaf segmentation in plant phenotyping: a collation study
- (2015) Hanno Scharr et al. MACHINE VISION AND APPLICATIONS
- A Review of Imaging Techniques for Plant Phenotyping
- (2014) Lei Li et al. SENSORS
- Future Scenarios for Plant Phenotyping
- (2013) Fabio Fiorani et al. Annual Review of Plant Biology
- An online database for plant image analysis software tools
- (2013) Guillaume Lobet et al. Plant Methods
- Phenomics – technologies to relieve the phenotyping bottleneck
- (2011) Robert T. Furbank et al. TRENDS IN PLANT SCIENCE
- Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging
- (2010) C. H. Bock et al. CRITICAL REVIEWS IN PLANT SCIENCES
- Time to automate identification
- (2010) Norman MacLeod et al. NATURE
- In the Eye of the Beholder: The Effect of Rater Variability and Different Rating Scales on QTL Mapping
- (2010) Jesse A. Poland et al. PHYTOPATHOLOGY
- Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning
- (2009) Thouis R. Jones et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Visual Rating and the Use of Image Analysis for Assessing Different Symptoms of Citrus Canker on Grapefruit Leaves
- (2008) C. H. Bock et al. PLANT DISEASE
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started