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IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

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 te...

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Autores principales: Kundu, Nidhi, Rani, Geeta, Dhaka, Vijaypal Singh, Gupta, Kalpit, Nayak, Siddaiah Chandra, Verma, Sahil, Ijaz, Muhammad Fazal, Woźniak, Marcin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397940/
https://www.ncbi.nlm.nih.gov/pubmed/34450827
http://dx.doi.org/10.3390/s21165386
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author Kundu, Nidhi
Rani, Geeta
Dhaka, Vijaypal Singh
Gupta, Kalpit
Nayak, Siddaiah Chandra
Verma, Sahil
Ijaz, Muhammad Fazal
Woźniak, Marcin
author_facet Kundu, Nidhi
Rani, Geeta
Dhaka, Vijaypal Singh
Gupta, Kalpit
Nayak, Siddaiah Chandra
Verma, Sahil
Ijaz, Muhammad Fazal
Woźniak, Marcin
author_sort Kundu, Nidhi
collection PubMed
description 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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spelling pubmed-83979402021-08-29 IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet Kundu, Nidhi Rani, Geeta Dhaka, Vijaypal Singh Gupta, Kalpit Nayak, Siddaiah Chandra Verma, Sahil Ijaz, Muhammad Fazal Woźniak, Marcin Sensors (Basel) Article 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. MDPI 2021-08-09 /pmc/articles/PMC8397940/ /pubmed/34450827 http://dx.doi.org/10.3390/s21165386 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kundu, Nidhi
Rani, Geeta
Dhaka, Vijaypal Singh
Gupta, Kalpit
Nayak, Siddaiah Chandra
Verma, Sahil
Ijaz, Muhammad Fazal
Woźniak, Marcin
IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
title IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
title_full IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
title_fullStr IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
title_full_unstemmed IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
title_short IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet
title_sort iot and interpretable machine learning based framework for disease prediction in pearl millet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397940/
https://www.ncbi.nlm.nih.gov/pubmed/34450827
http://dx.doi.org/10.3390/s21165386
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