Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification

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

Bibliographic Details
Main Authors:	Labiba, Zaima, Heram, Afrin A, Hossain, Md.Muhtasim, Alam, Sharia, Shakal, Binita Khan
Other Authors:	Chakrabarty, Amitabha
Format:	Thesis
Language:	English
Published:	Brac University 2024
Subjects:	Mango leaf classification Mango variations Convolutional neural network Vision transformer Data mining Neural networks (Computer science)
Online Access:	http://hdl.handle.net/10361/23586

id	10361-23586
record_format	dspace
spelling	10361-235862024-10-02T21:06:41Z Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification Labiba, Zaima Heram, Afrin A Hossain, Md.Muhtasim Alam, Sharia Shakal, Binita Khan Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Mango leaf classification Mango variations Convolutional neural network Vision transformer Data mining 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 75-77). Mango, often referred to as the “King of fruits”, occupies a superior place in the global agricultural landscape due to its growing demand. Thus accurate identification and classification of mango tree varieties is essential to improve quality control and inventory management in this context. In this study, we harness the power of well-established deep learning models, to detect the type and variety of mango leaves by using the mango leaf image processing method. Our meticulous analysis of accuracy and loss curves provides insight into model performance, ensuring the model is not overfitted. Additionally, we construct a comprehensive confusion matrix, highlighting the system’s ability to distinguish between different mango tree varieties. We also introduced a detailed classification report, offering precision, recall, F1 score, and support for each mango tree variety. This report is a valuable tool for stakeholders, helping them make informed decisions about quality control and inventory management. Notably, we curated a vast dataset of 14,000 raw mango leaf images, collected from different locations and seasons, reflecting the diversity of mango cultivation. Our database contains 26 types of different mango leaf variants. In the proposed system, various Deep Learning and Machine Learning algorithms were utilized including VGG16, EfficientNetB3, MobileNetV2, InceptionV3, Xception, ResNet50 and ViT for classification, and a comparison was made based on their accuracy rate which is respectively 98.64%, 87.19%, 97.90%, 98.89%, 98.42%, 98.10% & 97%. By combining precision curves, loss curves, confusion matrices, and classification reports, we provide a comprehensive performance evaluation of our system. This work will bring a cathartic change in our agricultural economy by easing the process of identifying mango plants. Zaima Labiba Afrin A Heram Md.Muhtasim Hossain Sharia Alam Binita Khan Shakal B.Sc in Computer Science 2024-06-25T10:21:56Z 2024-06-25T10:21:56Z ©2023 2023-09 Thesis ID 19101284 ID 22341059 ID 19101263 ID 19201032 ID 19301145 http://hdl.handle.net/10361/23586 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. 87 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Mango leaf classification Mango variations Convolutional neural network Vision transformer Data mining Neural networks (Computer science)
spellingShingle	Mango leaf classification Mango variations Convolutional neural network Vision transformer Data mining Neural networks (Computer science) Labiba, Zaima Heram, Afrin A Hossain, Md.Muhtasim Alam, Sharia Shakal, Binita Khan Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification
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	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Labiba, Zaima Heram, Afrin A Hossain, Md.Muhtasim Alam, Sharia Shakal, Binita Khan
format	Thesis
author	Labiba, Zaima Heram, Afrin A Hossain, Md.Muhtasim Alam, Sharia Shakal, Binita Khan
author_sort	Labiba, Zaima
title	Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification
title_short	Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification
title_full	Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification
title_fullStr	Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification
title_full_unstemmed	Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification
title_sort	evaluating cnn and vvsion transformer models for mango leaf variety identification
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23586
work_keys_str_mv	AT labibazaima evaluatingcnnandvvsiontransformermodelsformangoleafvarietyidentification AT heramafrina evaluatingcnnandvvsiontransformermodelsformangoleafvarietyidentification AT hossainmdmuhtasim evaluatingcnnandvvsiontransformermodelsformangoleafvarietyidentification AT alamsharia evaluatingcnnandvvsiontransformermodelsformangoleafvarietyidentification AT shakalbinitakhan evaluatingcnnandvvsiontransformermodelsformangoleafvarietyidentification
_version_	1814309876912881664

Evaluating CNN and Vvsion transformer models for Mango Leaf variety identification

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