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dc.contributor.advisorHossain, Muhammad Iqbal
dc.contributor.authorKhanom, Nazifa
dc.date.accessioned2024-09-09T06:23:44Z
dc.date.available2024-09-09T06:23:44Z
dc.date.copyright©2024
dc.date.issued2024-05
dc.identifier.otherID 24173004
dc.identifier.urihttp://hdl.handle.net/10361/24033
dc.descriptionThis project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024.en_US
dc.descriptionCataloged from the PDF version of the project report.
dc.descriptionIncludes bibliographical references (page 38).
dc.description.abstractEvery year, there is significant crop loss in developing countries due to delays in identifying plant diseases. Prompt and accurate identification of these diseases, with less reliance on field experts, could greatly mitigate this issue. Recognizing plant diseases correctly, particularly when they present similar leaf textures, poses a significant challenge. It’s crucial to consider factors such as leaf color and various texture features to accurately predict plant defects. The objective of this project is to employ Deep Learning methodologies for the detection of plant diseases based on leaf images. Deep learning, specifically Convolutional Neural Networks, is chosen due to its effectiveness in extracting features from plant leaves, making it well-suited for image data analysis in this context.en_US
dc.description.statementofresponsibilityNazifa Khanom
dc.format.extent45 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University project reports 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.
dc.subjectLeaf texturesen_US
dc.subjectTexture featuresen_US
dc.subjectCNNen_US
dc.subjectConvolutional neural networken_US
dc.subjectImage data analysisen_US
dc.subjectDeep learningen_US
dc.subjectDisease detection
dc.subject.lcshPlant diseases--Diagnosis.
dc.subject.lcshNeural networks (Computer science)--Agricultural aspects.
dc.subject.lcshArtificial intelligence--Applications in agriculture.
dc.titlePlantGuard: intelligent plant disease detectionen_US
dc.typeProject reporten_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeM.Sc. in Computer Science and Engineering


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