The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Disease detection on the leaves of the tomato plants by using deep learning
Abstract
The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or autonomously on the field or in the greenhouse. Likewise, diseases can also be detected from close-up photographs taken from plants by sensors built in fabricated greenhouses. The examined diseases in this study cause physical changes in the leaves of the tomato plant. These changes on the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architectures were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf images from the PlantVillage dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet.
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Cites
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Caffe: Convolutional Architecture for Fast Feature Embedding
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
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Using Deep Learning for Image-Based Plant Disease Detection
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Caffe: Convolutional Architecture for Fast Feature Embedding
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Cited by
Sharma, P., Berwal, Y. P. S., & Ghai, W. (2020) · Information Processing in Agriculture · doi:10.1016/j.inpa.2019.11.001 · Cited by 361
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
Metadata
- Authors
- Durmus, H., Gunes, E. O., & Kirci, M.
- Venue
- 2017 6th International Conference on Agro-Geoinformatics
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 1-5
- OpenAlex
- W2758893285
- Cited by
- 544
- References
- 13
- FWCI
- 64.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Durmus, H., Gunes, E. O., & Kirci, M. (2017). Disease detection on the leaves of the tomato plants by using deep learning. 2017 6th International Conference on Agro-Geoinformatics. https://doi.org/10.1109/agro-geoinformatics.2017.8047016
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