Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District.
This final-year project endeavors to pioneer an image-based solution for the early detection of diseases affecting coffee crops, employing advanced techniques in computer science. With a focus on precision agriculture, the project aims to develop a robust system capable of accurately identifying and...
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| Format: | Thesis |
| Language: | English |
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Kabale University
2024
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| Online Access: | http://hdl.handle.net/20.500.12493/2498 |
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| _version_ | 1823844184115642368 |
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| author | Asiimwe, Mark Kakuru, Ambrose |
| author_facet | Asiimwe, Mark Kakuru, Ambrose |
| author_sort | Asiimwe, Mark |
| collection | KAB-DR |
| description | This final-year project endeavors to pioneer an image-based solution for the early detection of diseases affecting coffee crops, employing advanced techniques in computer science. With a focus on precision agriculture, the project aims to develop a robust system capable of accurately identifying and classifying various coffee diseases through the analysis of plant images. The methodology involves acquiring high-resolution images, employing pre-processing methods to enhance data quality, and implementing state-of-the-art machine-learning algorithms for disease classification. The proposed system seeks to surpass existing methods by achieving higher efficiency in disease detection. The innovative aspect of this research lies in the integration of computer science methodologies to address a crucial challenge in agriculture. By contributing to the development of smart farming technologies, this project aims to empower coffee farmers with a tool for proactive disease management, ultimately improving crop yield and sustainability. Anticipated outcomes include a reliable image-based system demonstrating superior performance in coffee disease detection. The significance of this research extends to the broader field of precision agriculture, showcasing the potential of technology to enhance crop health and contribute to global food security. |
| format | Thesis |
| id | oai:idr.kab.ac.ug:20.500.12493-2498 |
| institution | KAB-DR |
| language | English |
| publishDate | 2024 |
| publisher | Kabale University |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-24982024-12-24T00:01:12Z Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. Asiimwe, Mark Kakuru, Ambrose Computer Science Image Processing Machine Learning Precision Agriculture Coffee Diseases Smart Farming This final-year project endeavors to pioneer an image-based solution for the early detection of diseases affecting coffee crops, employing advanced techniques in computer science. With a focus on precision agriculture, the project aims to develop a robust system capable of accurately identifying and classifying various coffee diseases through the analysis of plant images. The methodology involves acquiring high-resolution images, employing pre-processing methods to enhance data quality, and implementing state-of-the-art machine-learning algorithms for disease classification. The proposed system seeks to surpass existing methods by achieving higher efficiency in disease detection. The innovative aspect of this research lies in the integration of computer science methodologies to address a crucial challenge in agriculture. By contributing to the development of smart farming technologies, this project aims to empower coffee farmers with a tool for proactive disease management, ultimately improving crop yield and sustainability. Anticipated outcomes include a reliable image-based system demonstrating superior performance in coffee disease detection. The significance of this research extends to the broader field of precision agriculture, showcasing the potential of technology to enhance crop health and contribute to global food security. 2024-12-23T09:20:31Z 2024-12-23T09:20:31Z 2024 Thesis Asiimwe, M., & Kakuru, A. (2024).Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. Kabale: Kabale University. http://hdl.handle.net/20.500.12493/2498 en Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf Kabale University |
| spellingShingle | Computer Science Image Processing Machine Learning Precision Agriculture Coffee Diseases Smart Farming Asiimwe, Mark Kakuru, Ambrose Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. |
| title | Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. |
| title_full | Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. |
| title_fullStr | Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. |
| title_full_unstemmed | Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. |
| title_short | Design of Coffee Disease Detection Web Application Using Image Processing: A Case Study of Kyabugimbi-Kajunju Cooperative Farms in Bushenyi District. |
| title_sort | design of coffee disease detection web application using image processing a case study of kyabugimbi kajunju cooperative farms in bushenyi district |
| topic | Computer Science Image Processing Machine Learning Precision Agriculture Coffee Diseases Smart Farming |
| url | http://hdl.handle.net/20.500.12493/2498 |
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