Citrus leaf disease detection by image processing

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Bibliografske podrobnosti
Main Authors:	Chowdhury, Mahir Faisal, Nondi, Amit, Zaman, Fardin, Akhter, Sium Ibn, Pathan, Tanjina Bilma
Drugi avtorji:	Karim, Dewan Ziaul
Format:	Thesis
Jezik:	English
Izdano:	Brac University 2024
Teme:	Deep learning CNN ResNet-50 VGG16 MobileNet-V2 InceptionResNet-V2 DenseNet-121 Machine learning Image processing > Congresses
Online dostop:	http://hdl.handle.net/10361/22781

id	10361-22781
record_format	dspace
spelling	10361-227812024-05-09T21:02:21Z Citrus leaf disease detection by image processing Chowdhury, Mahir Faisal Nondi, Amit Zaman, Fardin Akhter, Sium Ibn Pathan, Tanjina Bilma Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Deep learning CNN ResNet-50 VGG16 MobileNet-V2 InceptionResNet-V2 DenseNet-121 Machine learning Image processing--Congresses This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 49-51). Citrus leaf diseases bring a danger to the earnings of citrus estates. When it comes to recovering from illness, early detection and accurate diagnosis are very necessary. In the last several decades, there have been advancements made in the diagnosis and classification of citrus leaf diseases via the use of deep learning techniques in image processing. When it comes to automating the detection of citrus leaf diseases, we recommend making use of pre-trained convolutional neural networks (CNNs) like ResNet-50, VGG16, MobileNet-V2, InceptionV3, InceptionResNet-V2, DenseNet- 201, and DenseNet-121.To accomplish this goal, a comprehensive data collection consisting of images of citrus leaves that have been identified will be gathered and pre-processed. Citrus canker, greening, and black spot leaves will be included in the databases, along with healthy and diseased citrus leaves.For the purpose of extracting useful characteristics from leaf images, we shall make use of deep learning models. For the purpose of picture classification, the models that were discussed before are useful and often used.In this research, we propose a CNN model that is both effective and efficient. The model was originally trained on 596 pictures, and then it was augmented with 2800 images that were divided into three categories: training, validation, and testing.70% of the data goes towards training, 15% goes towards validation, and 15% goes towards testing.A few pieces of public data will be served this model.Following that, we will evaluate the findings in relation to the prebuilt models.Last but not least, we get a training accuracy of 95.95% and a validation accuracy of 97.84%. Furthermore, our suggested model has a lower number of training parameters compared to all other pretrained models, which enables our model to categorise illnesses more quickly.This provides us with a decent level of accuracy. Mahir Faisal Chowdhury Amit Nondi Fardin Zaman Sium Ibn Akhter Tanjina Bilma Pathan B.Sc. in Computer Science 2024-05-09T03:08:19Z 2024-05-09T03:08:19Z ©2024 2024-01 Thesis ID: 19301026 ID: 19101247 ID: 20301473 ID: 20101566 ID: 19101617 http://hdl.handle.net/10361/22781 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. 63 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning CNN ResNet-50 VGG16 MobileNet-V2 InceptionResNet-V2 DenseNet-121 Machine learning Image processing--Congresses
spellingShingle	Deep learning CNN ResNet-50 VGG16 MobileNet-V2 InceptionResNet-V2 DenseNet-121 Machine learning Image processing--Congresses Chowdhury, Mahir Faisal Nondi, Amit Zaman, Fardin Akhter, Sium Ibn Pathan, Tanjina Bilma Citrus leaf disease detection by image processing
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Karim, Dewan Ziaul
author_facet	Karim, Dewan Ziaul Chowdhury, Mahir Faisal Nondi, Amit Zaman, Fardin Akhter, Sium Ibn Pathan, Tanjina Bilma
format	Thesis
author	Chowdhury, Mahir Faisal Nondi, Amit Zaman, Fardin Akhter, Sium Ibn Pathan, Tanjina Bilma
author_sort	Chowdhury, Mahir Faisal
title	Citrus leaf disease detection by image processing
title_short	Citrus leaf disease detection by image processing
title_full	Citrus leaf disease detection by image processing
title_fullStr	Citrus leaf disease detection by image processing
title_full_unstemmed	Citrus leaf disease detection by image processing
title_sort	citrus leaf disease detection by image processing
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22781
work_keys_str_mv	AT chowdhurymahirfaisal citrusleafdiseasedetectionbyimageprocessing AT nondiamit citrusleafdiseasedetectionbyimageprocessing AT zamanfardin citrusleafdiseasedetectionbyimageprocessing AT akhtersiumibn citrusleafdiseasedetectionbyimageprocessing AT pathantanjinabilma citrusleafdiseasedetectionbyimageprocessing
_version_	1814308341912961024

Citrus leaf disease detection by image processing

Podobne knjige/članki