Coffee wilt diagnosis application (CWDA)
| dc.contributor.author | Nawanyana, Leonida | |
| dc.contributor.author | Mugoya, Steven Raymond | |
| dc.contributor.author | Otema, Aaron | |
| dc.date.accessioned | 2020-01-10T09:36:58Z | |
| dc.date.available | 2020-01-10T09:36:58Z | |
| dc.date.issued | 2018-06 | |
| dc.identifier.citation | IEEE citation style | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/8313 | |
| dc.description | A project report submitted to the school of computing and informatics technology for the study leading to a project in partial fulfillment of the requirements for the award of the degree of bachelor of science in software engineering of Makerere University | en_US |
| dc.description.abstract | This project focuses on detecting or diagnosing Coffee Wilt Disease (CWD) on coffee plants by mainly farmers in Uganda. We came up with a working solution that could help them to easily diagnose CWD while in their plantations. The data were collected from individuals in Mukono district by using pre-tested questionnaires and interview guides. Descriptive statistics such as mean and percentage and numerical analysis were employed to analyze average total cost of coffee production before and after being hit by CWD. The levels of education attained by the farmer and the skills or training attended by the farmer were also assessed to help us in the design of our system. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere university | en_US |
| dc.subject | Coffee | en_US |
| dc.subject | Wilt | en_US |
| dc.subject | Diagnosis | en_US |
| dc.subject | Farmers | en_US |
| dc.title | Coffee wilt diagnosis application (CWDA) | en_US |
| dc.type | Thesis | en_US |