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Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriate...

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Autores principales: Trivedi, Naresh K., Gautam, Vinay, Anand, Abhineet, Aljahdali, Hani Moaiteq, Villar, Santos Gracia, Anand, Divya, Goyal, Nitin, Kadry, Seifedine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659659/
https://www.ncbi.nlm.nih.gov/pubmed/34883991
http://dx.doi.org/10.3390/s21237987
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author Trivedi, Naresh K.
Gautam, Vinay
Anand, Abhineet
Aljahdali, Hani Moaiteq
Villar, Santos Gracia
Anand, Divya
Goyal, Nitin
Kadry, Seifedine
author_facet Trivedi, Naresh K.
Gautam, Vinay
Anand, Abhineet
Aljahdali, Hani Moaiteq
Villar, Santos Gracia
Anand, Divya
Goyal, Nitin
Kadry, Seifedine
author_sort Trivedi, Naresh K.
collection PubMed
description Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.
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spelling pubmed-86596592021-12-10 Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network Trivedi, Naresh K. Gautam, Vinay Anand, Abhineet Aljahdali, Hani Moaiteq Villar, Santos Gracia Anand, Divya Goyal, Nitin Kadry, Seifedine Sensors (Basel) Article Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate. MDPI 2021-11-30 /pmc/articles/PMC8659659/ /pubmed/34883991 http://dx.doi.org/10.3390/s21237987 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
Trivedi, Naresh K.
Gautam, Vinay
Anand, Abhineet
Aljahdali, Hani Moaiteq
Villar, Santos Gracia
Anand, Divya
Goyal, Nitin
Kadry, Seifedine
Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network
title Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network
title_full Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network
title_fullStr Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network
title_full_unstemmed Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network
title_short Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network
title_sort early detection and classification of tomato leaf disease using high-performance deep neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659659/
https://www.ncbi.nlm.nih.gov/pubmed/34883991
http://dx.doi.org/10.3390/s21237987
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