The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Factors influencing the use of deep learning for plant disease recognition
Abstract
Deep learning is quickly becoming one of the most important tools for image classification. This technology is now beginning to be applied to the tasks of plant disease classification and recognition. The positive results that are being obtained using this approach hide some issues that are seldom taken into account in the respective experiments. This article presents an investigation into the main factors that affect the design and effectiveness of deep neural nets applied to plant pathology. An in-depth analysis of the subject, in which advantages and shortcomings are highlighted, should lead to more realistic conclusions on the subject. The arguments used throughout the text are built upon both studies found in the literature and experiments carried out using an image database carefully built to reflect and reproduce many of the conditions expected to be found in practice. This database, which contains almost 50,000 images, is being made freely available for academic purposes.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,133
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
A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-009-5152-4 · Cited by 3,515
Deep learning models for plant disease detection and diagnosis
Ferentinos, K. P. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.01.009 · Cited by 3,390
Livescu, K., Bezman, A., Borges, N., Yung, L.-Y., Çetin, Ö., Frankel, J., King, S., Magimai.-Doss, M., Chi, X., & Lavoie, L. (2007) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 2,145
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
Deep Learning of Representations for Unsupervised and Transfer Learning.
Bengio, Y. (2011) · Cited by 1,080
Identification of rice diseases using deep convolutional neural networks
Lu, Y., Yi, S., Zeng, N., Liu, Y., & Zhang, Y. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.06.023 · Cited by 1,059
Cited by
Using deep transfer learning for image-based plant disease identification
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105393 · Cited by 1,052
Barbedo, J. G. A. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.08.013 · Cited by 750
Self-citationDeep Learning-Based Leaf Disease Detection in Crops Using Images for Agricultural Applications
J., A., Eunice, J., Popescu, D. E., Chowdary, M. K., & Hemanth, J. (2022) · Agronomy · doi:10.3390/agronomy12102395 · Cited by 409
Barbedo, J. (2019) · Drones · doi:10.3390/drones3020040 · Cited by 391
Self-citationConvolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review
Jiang, Y., & Li, C. (2020) · Plant Phenomics · doi:10.34133/2020/4152816 · Cited by 380
Metadata
- Authors
- Barbedo, J. G. A.
- Venue
- Biosystems Engineering
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 172
- Pages
- 84-91
- ISSN
- 1537-5110 (ISSN-L)
- OpenAlex
- W2808709127
- Cited by
- 649
- References
- 22
- FWCI
- 36.41
- Type
- journal article
- Language
- en
- Open access
- closed
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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).
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