| dc.contributor.author | Ramcharan, A. |
| dc.contributor.author | Baranowski, K. |
| dc.contributor.author | McCloskey, P. |
| dc.contributor.author | Ahmed, B. |
| dc.contributor.author | Legg, J.P. |
| dc.contributor.author | Hughes, D.P. |
| dc.date.accessioned | 2019-12-04T11:11:03Z |
| dc.date.available | 2019-12-04T11:11:03Z |
| dc.date.issued | 2017 |
| dc.identifier.citation | Ramcharan, A., Baranowski, K., McCloskey, P., Ahamed, B., Legg, J. & Hughes, D.P. (2017). Deep learning for image-based cassava disease detection. Frontiers in Plant Science, 8, 1-7. |
| dc.identifier.issn | 1664-462X |
| dc.identifier.uri | https://hdl.handle.net/20.500.12478/2337 |
| dc.description | Open Access Journal; Published online: 27 Oct 2017 |
| dc.description.abstract | 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. |
| dc.format.extent | 1-7 |
| dc.language.iso | en |
| dc.subject | Cassava |
| dc.subject | Food Security |
| dc.subject | Disease Control |
| dc.subject | Epidemiology |
| dc.subject | Deep Learning |
| dc.subject | Convolutional Neural Networks |
| dc.subject | Transfer Learning |
| dc.subject | Mobile Epidemiology |
| dc.subject | Inception V3 Model |
| dc.title | Deep learning for image-based cassava disease detection |
| dc.type | Journal Article |
| dc.description.version | Peer Review |
| cg.contributor.crp | Climate Change, Agriculture and Food Security |
| cg.contributor.crp | Genebanks |
| cg.contributor.affiliation | Pennsylvania State University |
| cg.contributor.affiliation | Pittsburgh University |
| cg.contributor.affiliation | International Institute of Tropical Agriculture |
| cg.coverage.region | Africa |
| cg.coverage.region | East Africa |
| cg.coverage.country | Tanzania |
| cg.isijournal | ISI Journal |
| cg.authorship.types | CGIAR and advanced research institute |
| cg.iitasubject | Cassava |
| cg.iitasubject | Food Security |
| cg.iitasubject | Plant Diseases |
| cg.journal | Frontiers in Plant Science |
| cg.howpublished | Formally Published |
| cg.accessibilitystatus | Open Access |
| local.dspaceid | 92097 |
| cg.targetaudience | Scientists |
| cg.identifier.doi | http://dx.doi.org/10.3389/fpls.2017.01852 |
