The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Deep Learning for Image-Based Cassava Disease Detection
Open accessAbstract
Cassava is the third largest source of carbohydrates for human food in the world but is vulnerable to virus diseases, which threaten to destabilize food security in sub-Saharan Africa. Novel methods of cassava disease detection are needed to support improved control which will prevent this crisis. Image recognition offers both a cost effective and scalable technology for disease detection. New deep learning models offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cassava disease images taken in the field in Tanzania, we applied transfer learning to train a deep convolutional neural network to identify three diseases and two types of pest damage (or lack thereof). The best trained model accuracies were 98% for brown leaf spot (BLS), 96% for red mite damage (RMD), 95% for green mite damage (GMD), 98% for cassava brown streak disease (CBSD), and 96% for cassava mosaic disease (CMD). The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.
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Cites
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
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
Self-citationMachine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images
Mwebaze, E., & Owomugisha, G. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0034 · Cited by 114
Cited by
Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019) · IEEE Access · doi:10.1109/access.2019.2914929 · Cited by 892
Applications for deep learning in ecology
Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724
New perspectives on plant disease characterization based on deep learning
Lee, S. H., Goëau, H., Bonnet, P., & Joly, A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105220 · Cited by 348
Gill, T., Gill, S. K., Saini, D. K., Chopra, Y., de Koff, J. P., & Sandhu, K. S. (2022) · Phenomics · doi:10.1007/s43657-022-00048-z · Cited by 322
Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture
Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2020) · Journal of The Electrochemical Society · doi:10.1149/2.0222003jes · Cited by 282
Metadata
- Authors
- Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P.
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2017-10-27
- Volume
- 8
- Pages
- 1852-1852
- ISSN
- 1664-462X (ISSN-L)
- OpenAlex
- W2735962203
- Cited by
- 833
- References
- 5
- FWCI
- 33.79
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep Learning for Image-Based Cassava Disease Detection. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2017.01852
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