The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Using Deep Learning for Image-Based Plant Disease Detection
Open accessAbstract
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Speeded-Up Robust Features (SURF)
Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.09.014 · Cited by 13,557 · cited 1×
“Previously, the traditional approach for image classification tasks has been based on hand-engineered features such as SIFT[18], HoG[19], SURF[20], etc., and then to use some form of learning algorithm in these feature spaces.”
MethodBackpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Cited by
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
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
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
Nguyen, Q. H., Ly, H.-B., Ho, L. S., Al-Ansari, N., Le, H. V., Tran, V. Q., Prakash, I., & Pham, B. T. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/4832864 · Cited by 624
Metadata
- Authors
- Mohanty, S. P., Hughes, D. P., & Salathé, M.
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2016-09-22
- Volume
- 7
- Pages
- 1419-1419
- ISSN
- 1664-462X (ISSN-L)
- OpenAlex
- W2473156356
- Cited by
- 5,026
- References
- 10
- FWCI
- 128.98
- Type
- journal article
- Language
- en
- Open access
- gold
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