Leaf classification by feature extraction using CNN

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.

Dades bibliogràfiques
Autors principals:	Bhuiyan, Md. Mazharul Islam, Nowshin, Jakia, Jaheen, Atkiya
Altres autors:	Chakrabarty, Amitabha
Format:	Thesis
Idioma:	en_US
Publicat:	Brac University 2020
Matèries:	Convolutional Neural Network CNN Classification
Accés en línia:	http://hdl.handle.net/10361/14052

id	10361-14052
record_format	dspace
spelling	10361-140522022-01-26T10:20:07Z Leaf classification by feature extraction using CNN Bhuiyan, Md. Mazharul Islam Nowshin, Jakia Jaheen, Atkiya Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Convolutional Neural Network CNN Classification This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019. Cataloged from PDF version of thesis. Includes bibliographical references (pages 31-33). Plants are an integral part of our nature. The identification and classification of plant leaves has always been a matter of interest for the botanists as well as the laymen. Classification of plant leaves will enable us to know the heritage and details of plants at a glance avoiding the duplication of popular names. This recognition system will be beneficial to different sectors of our society including botanic research, medical field, the study of plant taxonomy etc. As leaves carry a lot of information about plant species, extraction of feature is a better way to classify the leaves. In this paper, we have proposed Convolutional Neural Network (CNN) and analyzed plant leaves with different models. We have collected the dataset from Kaggle. By preprocessing the images and extracting the features we have trained our pre-trained model. In our research, we have chosen three models of CNN which are InceptionV3, VGG16 and MobileNet. MobileNet achieved the highest accuracy of 69.47% with a mean absolute error of 30.26, while VGG16 achieved the lowest accuracy of 57.05% with a mean absolute error of 42.95 and 66.13% accuracy for Inception V3. Md. Mazharul Islam Bhuiyan Jakia Nowshin Atkiya Jaheen B. Computer Science 2020-10-11T05:09:45Z 2020-10-11T05:09:45Z 2019 2019-12 Thesis ID: 15201042 ID: 15201021 ID: 15301118 http://hdl.handle.net/10361/14052 en_US 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. 33 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Convolutional Neural Network CNN Classification
spellingShingle	Convolutional Neural Network CNN Classification Bhuiyan, Md. Mazharul Islam Nowshin, Jakia Jaheen, Atkiya Leaf classification by feature extraction using CNN
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Bhuiyan, Md. Mazharul Islam Nowshin, Jakia Jaheen, Atkiya
format	Thesis
author	Bhuiyan, Md. Mazharul Islam Nowshin, Jakia Jaheen, Atkiya
author_sort	Bhuiyan, Md. Mazharul Islam
title	Leaf classification by feature extraction using CNN
title_short	Leaf classification by feature extraction using CNN
title_full	Leaf classification by feature extraction using CNN
title_fullStr	Leaf classification by feature extraction using CNN
title_full_unstemmed	Leaf classification by feature extraction using CNN
title_sort	leaf classification by feature extraction using cnn
publisher	Brac University
publishDate	2020
url	http://hdl.handle.net/10361/14052
work_keys_str_mv	AT bhuiyanmdmazharulislam leafclassificationbyfeatureextractionusingcnn AT nowshinjakia leafclassificationbyfeatureextractionusingcnn AT jaheenatkiya leafclassificationbyfeatureextractionusingcnn
_version_	1814309199365013504

Leaf classification by feature extraction using CNN

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