An efficient deep learning approach to detect citrus leaves disease

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Podrobná bibliografie
Hlavní autoři:	Emon, Shaharear Hossain, Islam, Iftea Khairul, Nahin, Tasfia Jahan, Ahmed, Ahnaf Mahdin
Další autoři:	Alam, Dr. Md. Ashraful
Médium:	Diplomová práce
Jazyk:	English
Vydáno:	Brac University 2024
Témata:	Citrus Diseases Image processing Classification Plant Cognitive learning theory (Deep learning)
On-line přístup:	http://hdl.handle.net/10361/23596

id	10361-23596
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spelling	10361-235962024-06-26T21:03:11Z An efficient deep learning approach to detect citrus leaves disease Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin Alam, Dr. Md. Ashraful Department of Computer Science and Engineering, Brac University Citrus Diseases Image processing Classification Plant Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-44). Bangladesh is one of the leading exporters of citrus. The country has been exporting citrus fruits to more than 60 countries annually. The main risk that citrus disease poses to crop yield is through contact with infected fruit. Identifying different dis eases of citrus leaves needs a huge time, work, and expertise. As a result, a new citrus disease detection technology must be developed. Infected crops need to be harvested as soon as possible before they rot. We have developed a useful technique in this study to use deep learning models to detect illness in citrus leaves. Using a unique ensemble approach, we are now able to train the model with different numbers of classes, excluding the best illnesses, and then worked together on the forecast. Each plant’s state is determined by taking a snapshot of its leaves and analyzing them. Data collection, pre-processing, segmentation, extraction, and classification are used to detect leaf disease. In this study, plant diseases were identified using photos of their leaves and segmentation and feature extraction algorithms. Our method can predict illnesses with an accuracy of 95% by combining many classifications, which represents a significant opportunity to save production losses. Shaharear Hossain Emon Iftea Khairul Islam Tasfia Jahan Nahin Ahnaf Mahdin Ahmed B.Sc in Computer Science 2024-06-26T05:21:20Z 2024-06-26T05:21:20Z 2023 2023-01 Thesis ID: 18201109 ID: 21101350 ID: 18201129 ID: 18201159 http://hdl.handle.net/10361/23596 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 44 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Citrus Diseases Image processing Classification Plant Cognitive learning theory (Deep learning)
spellingShingle	Citrus Diseases Image processing Classification Plant Cognitive learning theory (Deep learning) Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin An efficient deep learning approach to detect citrus leaves disease
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Alam, Dr. Md. Ashraful
author_facet	Alam, Dr. Md. Ashraful Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin
format	Thesis
author	Emon, Shaharear Hossain Islam, Iftea Khairul Nahin, Tasfia Jahan Ahmed, Ahnaf Mahdin
author_sort	Emon, Shaharear Hossain
title	An efficient deep learning approach to detect citrus leaves disease
title_short	An efficient deep learning approach to detect citrus leaves disease
title_full	An efficient deep learning approach to detect citrus leaves disease
title_fullStr	An efficient deep learning approach to detect citrus leaves disease
title_full_unstemmed	An efficient deep learning approach to detect citrus leaves disease
title_sort	efficient deep learning approach to detect citrus leaves disease
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23596
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An efficient deep learning approach to detect citrus leaves disease

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