Detection of rice plant disease from RGB and grayscale images using an LW17 deep learning model

Yogesh Kumar Rathore; Rekh Ram Janghel; Chetan Swarup; Saroj Kumar Pandey; Ankit Kumar; Kamred Udham Singh; Teekam Singh; Yogesh Kumar Rathore; Rekh Ram Janghel; Chetan Swarup; Saroj Kumar Pandey; Ankit Kumar; Kamred Udham Singh; Teekam Singh

doi:10.3934/era.2023142

Electronic Research Archive

2023, Volume 31, Issue 5: 2813-2833. doi: 10.3934/era.2023142

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Detection of rice plant disease from RGB and grayscale images using an LW17 deep learning model

1.
Department of Computer Science & Engineering, National Institute of Technology, Raipur, India
2.
Department of Basic Science, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh-Male Campus 13316, Saudi Arabia
3.
Department of Computer Engineering & Application, GLA University Mathura, UP, India
4.
School of Computing, Graphic Era Hill University, Bell Road, Dehradun, Uttarakhand 248002, India
5.
Department of Computer Science and Engineering Graphic Era Deemed to be University, Dehradun 248002, India

Academic Editor: William Guo

Received: 15 December 2022 Revised: 09 February 2023 Accepted: 10 February 2023 Published: 14 March 2023

Rice is grown almost everywhere in the world, especially in Asian countries, because it is part of the diets of about half of the world's population. However, farmers and planting experts have faced several persistent agricultural obstacles for many years, including many rice diseases. Severe rice diseases might result in no grain harvest; hence, in the field of agriculture, a fast, automatic, less expensive, and reliable approach to identifying rice diseases is widely needed. This paper focuses on how to build a lightweight deep learning model to detect rice plant diseases more precisely. To achieve the above objective, we created our own CNN model "LW17" to detect rice plant disease more precisely in comparison to some of the pre-trained models, such as VGG19, InceptionV3, MobileNet, Xception, DenseNet201, etc. Using the proposed methodology, we took UCI datasets for disease detection and tested our model with different layers, different training–testing ratios, different pooling layers, different optimizers, different learning rates, and different epochs. The Light Weight 17 (LW17) model reduced the complexity and computation cost compared to other heavy deep learning models. We obtained the best accuracy of 93.75% with the LW17 model using max pooling with the "Adam" optimizer at a learning rate of 0.001. The model outperformed the other state-of-the-art models with a limited number of layers in the architecture.
- rice diseases,
- transfer learning,
- UCI datasets,
- deep learning,
- InceptionV3,
- DenseNet201,
- VGG19
Citation: Yogesh Kumar Rathore, Rekh Ram Janghel, Chetan Swarup, Saroj Kumar Pandey, Ankit Kumar, Kamred Udham Singh, Teekam Singh. Detection of rice plant disease from RGB and grayscale images using an LW17 deep learning model[J]. Electronic Research Archive, 2023, 31(5): 2813-2833. doi: 10.3934/era.2023142

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Abstract

Rice is grown almost everywhere in the world, especially in Asian countries, because it is part of the diets of about half of the world's population. However, farmers and planting experts have faced several persistent agricultural obstacles for many years, including many rice diseases. Severe rice diseases might result in no grain harvest; hence, in the field of agriculture, a fast, automatic, less expensive, and reliable approach to identifying rice diseases is widely needed. This paper focuses on how to build a lightweight deep learning model to detect rice plant diseases more precisely. To achieve the above objective, we created our own CNN model "LW17" to detect rice plant disease more precisely in comparison to some of the pre-trained models, such as VGG19, InceptionV3, MobileNet, Xception, DenseNet201, etc. Using the proposed methodology, we took UCI datasets for disease detection and tested our model with different layers, different training–testing ratios, different pooling layers, different optimizers, different learning rates, and different epochs. The Light Weight 17 (LW17) model reduced the complexity and computation cost compared to other heavy deep learning models. We obtained the best accuracy of 93.75% with the LW17 model using max pooling with the "Adam" optimizer at a learning rate of 0.001. The model outperformed the other state-of-the-art models with a limited number of layers in the architecture.

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