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A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)
The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648786/ https://www.ncbi.nlm.nih.gov/pubmed/37960385 http://dx.doi.org/10.3390/s23218685 |
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author | Debnath, Anjan Hasan, Md. Mahedi Raihan, M. Samrat, Nadim Alsulami, Mashael M. Masud, Mehedi Bairagi, Anupam Kumar |
author_facet | Debnath, Anjan Hasan, Md. Mahedi Raihan, M. Samrat, Nadim Alsulami, Mashael M. Masud, Mehedi Bairagi, Anupam Kumar |
author_sort | Debnath, Anjan |
collection | PubMed |
description | The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset. |
format | Online Article Text |
id | pubmed-10648786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106487862023-10-24 A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI) Debnath, Anjan Hasan, Md. Mahedi Raihan, M. Samrat, Nadim Alsulami, Mashael M. Masud, Mehedi Bairagi, Anupam Kumar Sensors (Basel) Article The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset. MDPI 2023-10-24 /pmc/articles/PMC10648786/ /pubmed/37960385 http://dx.doi.org/10.3390/s23218685 Text en © 2023 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 Debnath, Anjan Hasan, Md. Mahedi Raihan, M. Samrat, Nadim Alsulami, Mashael M. Masud, Mehedi Bairagi, Anupam Kumar A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI) |
title | A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI) |
title_full | A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI) |
title_fullStr | A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI) |
title_full_unstemmed | A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI) |
title_short | A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI) |
title_sort | smartphone-based detection system for tomato leaf disease using efficientnetv2b2 and its explainability with artificial intelligence (ai) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648786/ https://www.ncbi.nlm.nih.gov/pubmed/37960385 http://dx.doi.org/10.3390/s23218685 |
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