IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet Article Swipe
Nidhi Kundu
,
Geeta Rani
,
Vijaypal Singh Dhaka
,
Kalpit Gupta
,
S. Chandra Nayak
,
Sahil Verma
,
Muhammad Fazal Ijaz
,
Marcin Woźniak
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21165386
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21165386
Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the ‘Automatic and Intelligent Data Collector and Classifier’ framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The ‘Custom-Net’ model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the ‘Custom-Net’. Furthermore, the impact of transfer learning on the ‘Custom-Net’ and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the ‘Custom-Net’ extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the ‘Custom-Net’ model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of ‘Custom-Net’ is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21165386
- https://www.mdpi.com/1424-8220/21/16/5386/pdf?version=1628523462
- OA Status
- gold
- Cited By
- 170
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3190789542
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3190789542Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s21165386Digital Object Identifier
- Title
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IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl MilletWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-08-09Full publication date if available
- Authors
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Nidhi Kundu, Geeta Rani, Vijaypal Singh Dhaka, Kalpit Gupta, S. Chandra Nayak, Sahil Verma, Muhammad Fazal Ijaz, Marcin WoźniakList of authors in order
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https://doi.org/10.3390/s21165386Publisher landing page
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https://www.mdpi.com/1424-8220/21/16/5386/pdf?version=1628523462Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/21/16/5386/pdf?version=1628523462Direct OA link when available
- Concepts
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Plant disease, Cloud computing, Computer science, Artificial intelligence, Machine learning, Deep learning, Classifier (UML), Data mining, Database, Operating system, Biotechnology, BiologyTop concepts (fields/topics) attached by OpenAlex
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170Total citation count in OpenAlex
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2025: 19, 2024: 37, 2023: 58, 2022: 49, 2021: 7Per-year citation counts (last 5 years)
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60Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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