| dc.contributor.author | Amutuhaire, Mujaidu | |
| dc.contributor.author | Kwizera, Nicholas | |
| dc.contributor.author | Nakasango, Shariffa | |
| dc.contributor.author | Wangota, Felix Daniel | |
| dc.date.accessioned | 2022-04-22T07:45:06Z | |
| dc.date.available | 2022-04-22T07:45:06Z | |
| dc.date.issued | 2021-12 | |
| dc.identifier.citation | Amutuhaire, M., Kwizera, N., Nakasango, S., & Wangota, F. D. (2021). The idealcrop system. (Unpublished undergraduate dissertation). Makerere University, Kampala, Uganda. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/11824 | |
| 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 report is for the implementation of the IdealCrop System, from its design to completion. It is a web-based machine learning application that provides a service to farmers or farm managers, enabling them to make informed decisions on crop selection and care, and crop variety options to choose from while considering climatic factors, NPK soil nutrients, and best crop yield on a Ugandan district level. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Farming | en_US |
| dc.subject | Crop | en_US |
| dc.title | The idealcrop system | en_US |
| dc.type | Thesis | en_US |
