The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Image-based phenotyping of plant disease symptoms
Open accessAbstract
Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant-pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Marr, D. (1982) · Cumulative Index of Computer Aided Architectural Design · Cited by 5,733
Barlow, H. B. (1983) · Journal of Mathematical Psychology · doi:10.1016/0022-2496(83)90030-5 · Cited by 5,424
Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo
Baker, N. R. (2008) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.59.032607.092759 · Cited by 4,639
Field high-throughput phenotyping: the new crop breeding frontier
Araus, J. L., & Cairns, J. E. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2013.09.008 · Cited by 1,817 · cited 2×
“In recent years, there has been considerable interest and progress in the development of platforms for quantitative, high-throughput plant phenotyping (Furbank and Tester, 2011; Dhondt et al., 2013; Fiorani and Schurr, 2013; Araus and Cairns, 2014; Granier and Vile, 2014).”
BackgroundPlant Disease: A Threat to Global Food Security
Strange, R. N., & Scott, P. R. (2005) · Annual Review of Phytopathology · doi:10.1146/annurev.phyto.43.113004.133839 · Cited by 1,802
Phenomics – technologies to relieve the phenotyping bottleneck
Furbank, R. T., & Tester, M. (2011) · Trends in Plant Science · doi:10.1016/j.tplants.2011.09.005 · Cited by 1,675
Pivoting the Plant Immune System from Dissection to Deployment
Dangl, J. L., Horvath, D. M., & Staskawicz, B. J. (2013) · Science · doi:10.1126/science.1236011 · Cited by 1,252
Future Scenarios for Plant Phenotyping
Fiorani, F., & Schurr, U. (2013) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-050312-120137 · Cited by 995
Bock, C. H., Poole, G. H., Parker, P. E., & Gottwald, T. R. (2010) · Critical Reviews in Plant Sciences · doi:10.1080/07352681003617285 · Cited by 964
Role of Stomata in Plant Innate Immunity and Foliar Bacterial Diseases
Melotto, M., Underwood, W., & He, S. Y. (2008) · Annual Review of Phytopathology · doi:10.1146/annurev.phyto.121107.104959 · Cited by 724
Cited by
Buschmann, A. H., Camus, C., Infante, J., Neori, A., Israel, Á., Hernández-González, M. C., Pereda, S. V., Gomez-Pinchetti, J. L., Golberg, A., Tadmor-Shalev, N., et al. (2017) · European Journal of Phycology · doi:10.1080/09670262.2017.1365175 · Cited by 767
Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning
Wang, G., Sun, Y., & Wang, J. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/2917536 · Cited by 746
Disease detection on the leaves of the tomato plants by using deep learning
Durmus, H., Gunes, E. O., & Kirci, M. (2017) · 2017 6th International Conference on Agro-Geoinformatics · doi:10.1109/agro-geoinformatics.2017.8047016 · Cited by 544
Close range hyperspectral imaging of plants: A review
Mishra, P., Asaari, M. S. M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2017.09.009 · Cited by 319
Jackulin, C., & Murugavalli, S. (2022) · Measurement: Sensors · doi:10.1016/j.measen.2022.100441 · Cited by 319
Metadata
- Authors
- Mutka, A. M., & Bart, R. S.
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2015-01-05
- Volume
- 5
- Pages
- 734-734
- ISSN
- 1664-462X (ISSN-L)
- OpenAlex
- W2073754472
- Cited by
- 317
- References
- 36
- FWCI
- 26.30
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Image-based phenotyping of plant disease symptoms. https://app.cochat.ai/papers/10.3389/fpls.2014.00734
- The paper
- Mutka, A. M., & Bart, R. S. (2015). Image-based phenotyping of plant disease symptoms. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2014.00734
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
