| dc.contributor.author | Arinda, Sophie | |
| dc.contributor.author | Otim, Natham Petum | |
| dc.contributor.author | Ambangira, Mark Mwesigwa | |
| dc.contributor.author | Abitegeka, Bridget | |
| dc.date.accessioned | 2024-01-08T09:28:34Z | |
| dc.date.available | 2024-01-08T09:28:34Z | |
| dc.date.issued | 2023-05 | |
| dc.identifier.citation | Arinda, S. et al. (2023). AI-based decision support system for improving rice crop yield and quality in Uganda 2023 (Unpublished undergraduate dissertation). Kampala: Makerere University. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/18102 | |
| dc.description | Research Report submitted to the School of Computing and Informatics Technology For the Study Leading to a Project Proposal in Partial Fulfilment of the Requirements for the Award of the Degree of Bachelor of Science in Computer Science Of Makerere University | en_US |
| dc.description.abstract | The Ugandan government has identified rice as a strategic crop to combat food insecurity and boost economic standards. A study will focus on the Ugandan rice sector, collecting data from historical records and focusing on rice farmers across the country. The data will include socioeconomic, agronomic, and environmental data. Socioeconomic data will include farm size, ownership, and management structure, while agronomic data will focus on crop management practices and environmental factors. The study will use deep learning techniques and consider yearly rice crop yields, regardless of the two major growing seasons. The data collection will be conducted across all commercial rice growing districts in Uganda. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Crop yield prediction | en_US |
| dc.subject | Decision Support Systems | en_US |
| dc.title | AI-based decision support system for improving rice crop yield and quality in Uganda 2023 | en_US |
| dc.type | Thesis | en_US |
