PlantGuard: intelligent plant disease detection
| dc.contributor.advisor | Hossain, Muhammad Iqbal | |
| dc.contributor.author | Khanom, Nazifa | |
| dc.contributor.department | Department of Computer Science and Engineering | |
| 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.description | Cataloged from the PDF version of the project report. | |
| dc.description | Includes bibliographical references (page 38). | |
| 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.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.degree | M.Sc. in Computer Science and Engineering | |
| dc.description.statementofresponsibility | Nazifa Khanom | |
| dc.format.extent | 45 pages | |
| dc.identifier.other | ID 24173004 | |
| dc.identifier.uri | http://hdl.handle.net/10361/24033 | |
| 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 | Disease detection | |
| dc.subject | Deep learning | en_US |
| 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 |