Detection and classification of Mango Leaf diseases utilizing convolutional neural network models

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

Библиографические подробности
Главные авторы:	Ferdous, Md.Rubaiat, Amin, Abdulla Al, Shaily, Senjuti Sarkar, Ricky, Fabliha Tarannum, Tabeeb, Tahmeed
Другие авторы:	Islam, Saiful
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2024
Предметы:	Mango leaf disease Convolutional neural network Deep learning Artificial Intelligence Machine learning Machine learning. Data mining. Artificial Intelligence. Convolutional neural networks. Plant diseases > Detection.
Online-ссылка:	http://hdl.handle.net/10361/24293

id	10361-24293
record_format	dspace
spelling	10361-242932024-10-02T21:03:37Z Detection and classification of Mango Leaf diseases utilizing convolutional neural network models Ferdous, Md.Rubaiat Amin, Abdulla Al Shaily, Senjuti Sarkar Ricky, Fabliha Tarannum Tabeeb, Tahmeed Islam, Saiful Tasnim, Anika Department of Computer Science and Engineering, Brac University Mango leaf disease Convolutional neural network Deep learning Artificial Intelligence Machine learning Machine learning. Data mining. Artificial Intelligence. Convolutional neural networks. Plant diseases--Detection. 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 40-43). Plant diseases, particularly those affecting crop plants, pose a significant danger to world food security by compromising the quality and yield of agricultural produce. Mango leaf disease is one such example. Mango leaf diseases are quite harmful since they can significantly lower crop yields of mangos, both in quantity and quality. Therefore, it is critical to identify leaf diseases in crops like mangos as soon as possible in order to take prompt preventive action. In large cultivated areas where mangoes are planted in significant quantities, the amount of manual inspection can be significantly reduced by mechanizing the process of disease recognition. With their exceptional ability to identify complex patterns in images, Convolutional Neural Networks (CNN) have great potential for automating the identification of illnesses affecting mango leaves. CNNs have been used in several studies with impressive accuracy rates, opening the door to improved crop management techniques and early disease diagnosis. In order to identify various illnesses, a research study using CNN—one of the most advanced deep learning algorithms—is presented in this work. CNN is used to segment and classify pictures of mango leaves. A number of CNN models, including Xception, MobileNetV2, ResNet50, DenseNet201, and ResNet50, have been used to accurately identify and categorize diseases, which will ultimately improve the production and health of mango crops. MangoLeafBD, a dataset that was acquired from Kaggle, has been used, and XAI (Explainable Artificial Intelligence), specifically Grad-CAM (Gradient-weighted Class Activation Mapping) and LIME (Local Interpretable Model-agnostic Explanations), were used to understand the rationale behind the decisions made by the applied models. Using these CNN models approach, it was observed that ResNet50 performs better than other models with 98% F1 score in identification and classification of mango leaf diseases. Md.Rubaiat Ferdous Abdulla Al Amin Senjuti Sarkar Shaily Fabliha Tarannum Ricky Tahmeed Tabeeb B.Sc. in Computer Science 2024-10-02T10:23:01Z 2024-10-02T10:23:01Z ©2024 2024-05 Thesis ID 19101299 ID 19301083 ID 20301359 ID 20301048 ID 20101582 http://hdl.handle.net/10361/24293 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. 46 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Mango leaf disease Convolutional neural network Deep learning Artificial Intelligence Machine learning Machine learning. Data mining. Artificial Intelligence. Convolutional neural networks. Plant diseases--Detection.
spellingShingle	Mango leaf disease Convolutional neural network Deep learning Artificial Intelligence Machine learning Machine learning. Data mining. Artificial Intelligence. Convolutional neural networks. Plant diseases--Detection. Ferdous, Md.Rubaiat Amin, Abdulla Al Shaily, Senjuti Sarkar Ricky, Fabliha Tarannum Tabeeb, Tahmeed Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Islam, Saiful
author_facet	Islam, Saiful Ferdous, Md.Rubaiat Amin, Abdulla Al Shaily, Senjuti Sarkar Ricky, Fabliha Tarannum Tabeeb, Tahmeed
format	Thesis
author	Ferdous, Md.Rubaiat Amin, Abdulla Al Shaily, Senjuti Sarkar Ricky, Fabliha Tarannum Tabeeb, Tahmeed
author_sort	Ferdous, Md.Rubaiat
title	Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
title_short	Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
title_full	Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
title_fullStr	Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
title_full_unstemmed	Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
title_sort	detection and classification of mango leaf diseases utilizing convolutional neural network models
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/24293
work_keys_str_mv	AT ferdousmdrubaiat detectionandclassificationofmangoleafdiseasesutilizingconvolutionalneuralnetworkmodels AT aminabdullaal detectionandclassificationofmangoleafdiseasesutilizingconvolutionalneuralnetworkmodels AT shailysenjutisarkar detectionandclassificationofmangoleafdiseasesutilizingconvolutionalneuralnetworkmodels AT rickyfablihatarannum detectionandclassificationofmangoleafdiseasesutilizingconvolutionalneuralnetworkmodels AT tabeebtahmeed detectionandclassificationofmangoleafdiseasesutilizingconvolutionalneuralnetworkmodels
_version_	1814308579155378176

Detection and classification of Mango Leaf diseases utilizing convolutional neural network models

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