Krishobondhu : an automated system for diagnosis of paddy disease
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.
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BRAC University
2014
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| Online pristup: | http://hdl.handle.net/10361/3754 |
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10361-37542022-01-26T10:21:47Z Krishobondhu : an automated system for diagnosis of paddy disease Rozario, Shovon Paulinus Rahman, Dr. Mohammad Zahidur Alom, Md. Zahangir Department of Computer Science and Engineering, BRAC University Computer science and engineering Crop disease management system Delta E Blob detection Euclidean distance Binary search algorithm This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. Cataloged from PDF version of thesis report. Includes bibliographical references (page 43 -44). This paper demonstrates ‘Krishokbondhu’, an automated system for the farmers to identify paddy diseases using their mobile phones. The uploaded pictures captured by the mobile phones are processed in the remote server and presented to an expert group for their opinion. Computer vision techniques are used for detection of affected spots from the image and their classification. A simple color difference based approach is followed for segmentation of the disease affected lesions. Blob detection algorithm is used for feature extraction from the segmented images where features like number of blobs in the crop, nitrogen level of the leaf, area and color values of the affected areas etc are used for classification of the diseases. Binary Search Tree is used for mapping the feature values for comparison of Euclidean distance during classification. The system allows the expert to evaluate the analysis results and provide feedbacks to the famers through a notification to their mobile phones. The mobile application has been developed for Windows Phone and the remote server application is developed using .NET framework. Shovon Paulinus Rozario B. Computer Science and Engineering 2014-10-02T05:13:48Z 2014-10-02T05:13:48Z 2014 2014-09 Thesis ID 11101074 http://hdl.handle.net/10361/3754 en BRAC University Internship 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. 49 pages application/pdf BRAC University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Computer science and engineering Crop disease management system Delta E Blob detection Euclidean distance Binary search algorithm |
| spellingShingle |
Computer science and engineering Crop disease management system Delta E Blob detection Euclidean distance Binary search algorithm Rozario, Shovon Paulinus Krishobondhu : an automated system for diagnosis of paddy disease |
| description |
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014. |
| author2 |
Rahman, Dr. Mohammad Zahidur |
| author_facet |
Rahman, Dr. Mohammad Zahidur Rozario, Shovon Paulinus |
| format |
Thesis |
| author |
Rozario, Shovon Paulinus |
| author_sort |
Rozario, Shovon Paulinus |
| title |
Krishobondhu : an automated system for diagnosis of paddy disease |
| title_short |
Krishobondhu : an automated system for diagnosis of paddy disease |
| title_full |
Krishobondhu : an automated system for diagnosis of paddy disease |
| title_fullStr |
Krishobondhu : an automated system for diagnosis of paddy disease |
| title_full_unstemmed |
Krishobondhu : an automated system for diagnosis of paddy disease |
| title_sort |
krishobondhu : an automated system for diagnosis of paddy disease |
| publisher |
BRAC University |
| publishDate |
2014 |
| url |
http://hdl.handle.net/10361/3754 |
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AT rozarioshovonpaulinus krishobondhuanautomatedsystemfordiagnosisofpaddydisease |
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