PlantGuard: intelligent plant disease detection
This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024.
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| Język: | English |
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Brac University
2024
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| Dostęp online: | http://hdl.handle.net/10361/24033 |
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10361-240332024-09-09T21:01:06Z PlantGuard: intelligent plant disease detection Khanom, Nazifa Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Leaf textures Texture features CNN Convolutional neural network Image data analysis Deep learning Disease detection Plant diseases--Diagnosis. Neural networks (Computer science)--Agricultural aspects. Artificial intelligence--Applications in agriculture. This project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024. Cataloged from the PDF version of the project report. Includes bibliographical references (page 38). 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. Nazifa Khanom M.Sc. in Computer Science and Engineering 2024-09-09T06:23:44Z 2024-09-09T06:23:44Z ©2024 2024-05 Project report ID 24173004 http://hdl.handle.net/10361/24033 en 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. 45 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Leaf textures Texture features CNN Convolutional neural network Image data analysis Deep learning Disease detection Plant diseases--Diagnosis. Neural networks (Computer science)--Agricultural aspects. Artificial intelligence--Applications in agriculture. |
| spellingShingle |
Leaf textures Texture features CNN Convolutional neural network Image data analysis Deep learning Disease detection Plant diseases--Diagnosis. Neural networks (Computer science)--Agricultural aspects. Artificial intelligence--Applications in agriculture. Khanom, Nazifa PlantGuard: intelligent plant disease detection |
| 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. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Khanom, Nazifa |
| format |
Project report |
| author |
Khanom, Nazifa |
| author_sort |
Khanom, Nazifa |
| title |
PlantGuard: intelligent plant disease detection |
| title_short |
PlantGuard: intelligent plant disease detection |
| title_full |
PlantGuard: intelligent plant disease detection |
| title_fullStr |
PlantGuard: intelligent plant disease detection |
| title_full_unstemmed |
PlantGuard: intelligent plant disease detection |
| title_sort |
plantguard: intelligent plant disease detection |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/24033 |
| work_keys_str_mv |
AT khanomnazifa plantguardintelligentplantdiseasedetection |
| _version_ |
1814307301604982784 |