dc.contributor.advisor | Hossain, Muhammad Iqbal | |
dc.contributor.author | Khanom, Nazifa | |
dc.date.accessioned | 2024-09-09T06:23:44Z | |
dc.date.available | 2024-09-09T06:23:44Z | |
dc.date.copyright | ©2024 | |
dc.date.issued | 2024-05 | |
dc.identifier.other | ID 24173004 | |
dc.identifier.uri | http://hdl.handle.net/10361/24033 | |
dc.description | This 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.description | Cataloged from the PDF version of the project report. | |
dc.description | Includes bibliographical references (page 38). | |
dc.description.abstract | Every 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.statementofresponsibility | Nazifa Khanom | |
dc.format.extent | 45 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac 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.subject | Leaf textures | en_US |
dc.subject | Texture features | en_US |
dc.subject | CNN | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Image data analysis | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Disease detection | |
dc.subject.lcsh | Plant diseases--Diagnosis. | |
dc.subject.lcsh | Neural networks (Computer science)--Agricultural aspects. | |
dc.subject.lcsh | Artificial intelligence--Applications in agriculture. | |
dc.title | PlantGuard: intelligent plant disease detection | en_US |
dc.type | Project report | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | M.Sc. in Computer Science and Engineering | |