Smart detection and classification of fungal disease in rice plants using image processing techniques

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

Bibliografiske detaljer
Main Authors:	Rashed, Akib, Ifraj, Sabista, Toa, Mashfia Zaman
Andre forfattere:	Ahmed, Md. Sabbir
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2024
Fag:	Fungal infection Disease detection Rice plant Machine learning Binary classification Plant diseases > Diagnosis. Image processing > Digital techniques. Sustainable agriculture.
Online adgang:	http://hdl.handle.net/10361/24338

id	10361-24338
record_format	dspace
spelling	10361-243382024-10-16T21:01:25Z Smart detection and classification of fungal disease in rice plants using image processing techniques Rashed, Akib Ifraj, Sabista Toa, Mashfia Zaman Ahmed, Md. Sabbir Dofadar, Dibyo Fabian Department of Computer Science and Engineering, Brac University Fungal infection Disease detection Rice plant Machine learning Binary classification Plant diseases--Diagnosis. Image processing--Digital techniques. Sustainable agriculture. 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 39-40). One of the most crucial staple crops, rice (Oryza Sativa), feeds a significant percentage of the world’s population. However, fungal infections, which may significantly reduce yields and affect global food security, represent an extreme risk to rice’s productivity and quality. We created a custom dataset of 991 images capturing both healthy and False smut affected rice plants. Several state-of-art deep learning models including ResNet50V2, AlexNet, VGG19, VGG16, InceptionV3, and CNN architecture were applied to classify the disease. The models were trained, validated and tested on our dataset, and the performance was analyzed based on metrics such as accuracy, precision, recall, and F1-score. Among all the models, Inception V3 achieved the highest result with an accuracy of 99.49%. The result of the research will further contribute to developing a web application for identifying and diagnosing fungal blasts in rice plants to ensure better rice cultivation, enabling early intervention and sustainable crop management practices. Akib Rashed Sabista Ifraj Mashfia Zaman Toa B.Sc. in Computer Science 2024-10-16T09:38:48Z 2024-10-16T09:38:48Z ©2024 2024-05 Thesis ID 20301220 ID 20301175 ID 20301229 http://hdl.handle.net/10361/24338 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. 50 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Fungal infection Disease detection Rice plant Machine learning Binary classification Plant diseases--Diagnosis. Image processing--Digital techniques. Sustainable agriculture.
spellingShingle	Fungal infection Disease detection Rice plant Machine learning Binary classification Plant diseases--Diagnosis. Image processing--Digital techniques. Sustainable agriculture. Rashed, Akib Ifraj, Sabista Toa, Mashfia Zaman Smart detection and classification of fungal disease in rice plants using image processing techniques
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Ahmed, Md. Sabbir
author_facet	Ahmed, Md. Sabbir Rashed, Akib Ifraj, Sabista Toa, Mashfia Zaman
format	Thesis
author	Rashed, Akib Ifraj, Sabista Toa, Mashfia Zaman
author_sort	Rashed, Akib
title	Smart detection and classification of fungal disease in rice plants using image processing techniques
title_short	Smart detection and classification of fungal disease in rice plants using image processing techniques
title_full	Smart detection and classification of fungal disease in rice plants using image processing techniques
title_fullStr	Smart detection and classification of fungal disease in rice plants using image processing techniques
title_full_unstemmed	Smart detection and classification of fungal disease in rice plants using image processing techniques
title_sort	smart detection and classification of fungal disease in rice plants using image processing techniques
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/24338
work_keys_str_mv	AT rashedakib smartdetectionandclassificationoffungaldiseaseinriceplantsusingimageprocessingtechniques AT ifrajsabista smartdetectionandclassificationoffungaldiseaseinriceplantsusingimageprocessingtechniques AT toamashfiazaman smartdetectionandclassificationoffungaldiseaseinriceplantsusingimageprocessingtechniques
_version_	1814307515909799936

Smart detection and classification of fungal disease in rice plants using image processing techniques

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