The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Deep learning for classification and severity estimation of coffee leaf biotic stress
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(2008) · Journal of Machine Learning Research · Cited by 33,051
mixup: Beyond Empirical Risk Minimization
Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv · doi:10.48550/arxiv.1710.09412 · Cited by 5,831
Using Deep Learning for Image-Based Plant Disease Detection
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,026
An Overview of Multi-Task Learning in Deep Neural Networks
Ruder, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05098 · Cited by 2,265
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,620
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition
Fuentes, A., Yoon, S., Kim, S., & Park, D. (2017) · Sensors · doi:10.3390/s17092022 · Cited by 1,572
A comparative study of fine-tuning deep learning models for plant disease identification
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.03.032 · Cited by 1,487
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
Hughes, D. P., & Salathé, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.08060 · Cited by 900
Metadata
- Authors
- Esgario, J. G. M., Krohling, R. A., & Ventura, J. A.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 169
- Pages
- 105162
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2998829203
- Cited by
- 339
- References
- 27
- FWCI
- 25.55
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Esgario, J. G. M., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2019.105162
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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.
