The use of Unmanned Aerial Vehicles (UAV) remotely sensed data and machine learning techniques to predict maize yield.
| dc.contributor.advisor | Odindi, John Odhiambo. | |
| dc.contributor.advisor | Mutanga, Onisimo. | |
| dc.contributor.advisor | Matongera, Trylee Nyasha. | |
| dc.contributor.author | Dlamini, Celuxolo Michal. | |
| dc.date.accessioned | 2024-06-27T09:34:35Z | |
| dc.date.available | 2024-06-27T09:34:35Z | |
| dc.date.created | 2024 | |
| dc.date.issued | 2024 | |
| dc.description | Masters Degree. University of KwaZulu-Natal, Pietermaritzburg. | |
| dc.description.abstract | Abstract available in PDF. | |
| dc.identifier.doi | https://doi.org/10.29086/10413/23174 | |
| dc.identifier.uri | https://hdl.handle.net/10413/23174 | |
| dc.language.iso | en | |
| dc.subject.other | Small-scale farming systems. | |
| dc.subject.other | Above-ground biomass. | |
| dc.title | The use of Unmanned Aerial Vehicles (UAV) remotely sensed data and machine learning techniques to predict maize yield. | |
| dc.type | Thesis |