The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques
Open accessAbstract
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the types of tomato diseases and Mask R-CNN is used to detect and segment the locations and shapes of the infected areas. To select the model that best fits the tomato disease detection task, four different deep convolutional neural networks are combined with the two object detection models. Data are collected from the Internet and the dataset is divided into a training set, a validation set, and a test set used in the experiments. The experimental results show that the proposed models can accurately and quickly identify the eleven tomato disease types and segment the locations and shapes of the infected areas.
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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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
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
Backpropagation 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
Plant diseases and pests detection based on deep learning: a review
Liu, J., & Wang, X. (2021) · Plant Methods · doi:10.1186/s13007-021-00722-9 · Cited by 1,011
A review of computer vision technologies for plant phenotyping
Li, Z., Guo, R., Li, M., Chen, Y., & Li, G. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105672 · Cited by 391
Bari, B. S., Islam, M. N., Rashid, M., Hasan, M. J., Razman, M. A. M., Musa, R. M., Ab Nasir, A. F., & P.P. Abdul Majeed, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.432 · Cited by 323
A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits
Dhiman, P., Kukreja, V., Manoharan, P., Kaur, A., Kamruzzaman, M. M., Dhaou, I. B., & Iwendi, C. (2022) · Electronics · doi:10.3390/electronics11030495 · Cited by 308
Metadata
- Authors
- Wang, Q., Qi, F., Sun, M., Qu, J., & Xue, J.
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2019-12-16
- Volume
- 2019
- Pages
- 1-15
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- OpenAlex
- W2995726119
- Cited by
- 180
- References
- 5
- FWCI
- 13.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Wang, Q., Qi, F., Sun, M., Qu, J., & Xue, J. (2019). Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2019/9142753
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