The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Performance analysis of deep learning CNN models for disease detection in plants using image segmentation
Open accessAbstract
Food security for the 7 billion people on earth requires minimizing crop damage by timely detection of diseases. Most deep learning models for automated detection of diseases in plants suffer from the fatal flaw that once tested on independent data, their performance drops significantly. This work investigates a potential solution to this problem by using segmented image data to train the convolutional neural network (CNN) models. As compared to the F-CNN model trained using full images, S-CNN model trained using segmented images more than doubles in performance to 98.6% accuracy when tested on independent data previously unseen by the models even with 10 disease classes. Not only this, by using tomato plant and target spot disease type as an example, we show that the confidence of self-classification for S-CNN model improves significantly over F-CNN model. This research work brings applicability of automated methods closer to non-experts for timely detection of diseases.
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Cites
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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
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
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
A comparative study of fine-tuning deep learning models for plant disease identification
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.03.032 · Cited by 1,487
Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
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Plant disease identification from individual lesions and spots using deep learning
Arnal Barbedo, J. G. (2019) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2019.02.002 · Cited by 783
Barbedo, J. G. A. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.08.013 · Cited by 750
Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning
Wang, G., Sun, Y., & Wang, J. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/2917536 · Cited by 746
Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks
Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2018) · IEEE Access · doi:10.1109/access.2018.2844405 · Cited by 656
Cited by
Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
Hasan, R. I., Yusuf, S. M., & Alzubaidi, L. (2020) · Plants · doi:10.3390/plants9101302 · Cited by 213
Metadata
- Authors
- Sharma, P., Berwal, Y. P. S., & Ghai, W.
- Venue
- Information Processing in Agriculture
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 7
- Issue
- 4
- Pages
- 566-574
- ISSN
- 2214-3173 (ISSN-L)
- OpenAlex
- W2985886590
- Cited by
- 361
- References
- 20
- FWCI
- 23.33
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sharma, P., Berwal, Y. P. S., & Ghai, W. (2020). Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2019.11.001
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