Tomato leaf disease detection using convolutional neural network

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

Detalhes bibliográficos
Main Authors:	Hasan, Md. Riazul, Hossain, Md. Shajib, Islam, Md. Minhajul, Rahman Apu, Md. Rejoanur, Moli, Farzana Akter
Outros Autores:	Rasel, Annajiat Alim
Formato:	Thesis
Idioma:	English
Publicado em:	Brac University 2023
Assuntos:	Deep learning Convolutional neural network ResNet-50 Inception v3 Tomato leaf disease detection Neural networks (Computer science)
Acesso em linha:	http://hdl.handle.net/10361/22005

id	10361-22005
record_format	dspace
spelling	10361-220052023-12-20T10:28:34Z Tomato leaf disease detection using convolutional neural network Hasan, Md. Riazul Hossain, Md. Shajib Islam, Md. Minhajul Rahman Apu, Md. Rejoanur Moli, Farzana Akter Rasel, Annajiat Alim Chakrabarty, Dr. Amitabha Department of Computer Science and Engineering, Brac University Deep learning Convolutional neural network ResNet-50 Inception v3 Tomato leaf disease detection Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 48-53). The fertile soil and easy access to water make agriculture more suitable and valu able for Bangladesh. Most people are directly or indirectly dependent on agricultural products for their livelihood. Agriculture plays an important role in the GDP of Bangladesh, which is 12.68% in 2019. According to the UN FAO, tomato is a type of vegetable that is ingested by 16% of the entire population. When analyzing the agricultural environment in Bangladesh, tomatoes are considered one of the most common vegetables. Plant infections pose a significant danger to crop production, yet timely detection remains a challenge in several regions of the world due to a lack of facilities. Climate changes are forcing us to take more care of agriculture to ensure food safety. Early detection of diseases has been made possible by cur rent developments in computer vision. Image processing and deep learning are very useful in this situation. The object’s impacted region is segmented using a bespoke threshold algorithm based on HBS (hue-based segmentation). Utilizing a color co occurrence approach, the segmented portion’s consequential selected features are recovered for edge detection. This research shows the diagnosis and detection of tomato leaf diseases involving several steps, including image capture, image pre processing, picture segmentation, feature extraction, and classification using a Con volutional Neural Network(CNN). The proposed CNN model achieved 95% accuracy while using much fewer computational resources, which makes it easily deployable in mobile applications. Md. Riazul Hasan Md. Shajib Hossain Md. Minhajul Islam Md. Rejoanur Rahman Apu Farzana Akter Moli B.Sc. in Computer Science and Engineering 2023-12-18T06:49:33Z 2023-12-18T06:49:33Z 2023 2023-01 Thesis ID: 19101550 ID: 19101250 ID: 19101111 ID: 19101260 ID: 19101280 http://hdl.handle.net/10361/22005 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. 53 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Convolutional neural network ResNet-50 Inception v3 Tomato leaf disease detection Neural networks (Computer science)
spellingShingle	Deep learning Convolutional neural network ResNet-50 Inception v3 Tomato leaf disease detection Neural networks (Computer science) Hasan, Md. Riazul Hossain, Md. Shajib Islam, Md. Minhajul Rahman Apu, Md. Rejoanur Moli, Farzana Akter Tomato leaf disease detection using convolutional neural network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Rasel, Annajiat Alim
author_facet	Rasel, Annajiat Alim Hasan, Md. Riazul Hossain, Md. Shajib Islam, Md. Minhajul Rahman Apu, Md. Rejoanur Moli, Farzana Akter
format	Thesis
author	Hasan, Md. Riazul Hossain, Md. Shajib Islam, Md. Minhajul Rahman Apu, Md. Rejoanur Moli, Farzana Akter
author_sort	Hasan, Md. Riazul
title	Tomato leaf disease detection using convolutional neural network
title_short	Tomato leaf disease detection using convolutional neural network
title_full	Tomato leaf disease detection using convolutional neural network
title_fullStr	Tomato leaf disease detection using convolutional neural network
title_full_unstemmed	Tomato leaf disease detection using convolutional neural network
title_sort	tomato leaf disease detection using convolutional neural network
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/22005
work_keys_str_mv	AT hasanmdriazul tomatoleafdiseasedetectionusingconvolutionalneuralnetwork AT hossainmdshajib tomatoleafdiseasedetectionusingconvolutionalneuralnetwork AT islammdminhajul tomatoleafdiseasedetectionusingconvolutionalneuralnetwork AT rahmanapumdrejoanur tomatoleafdiseasedetectionusingconvolutionalneuralnetwork AT molifarzanaakter tomatoleafdiseasedetectionusingconvolutionalneuralnetwork
_version_	1814308266011787264

Tomato leaf disease detection using convolutional neural network

Registos relacionados