Zinhle Mashaba-Munghemezulu

A Comparative Assessment of Machine and Deep Learning Approaches for Grassland Mapping with Sentinel-1, Sentinel-2 and Ancillary Data

Princess Khoza, Zinhle Mashaba-Munghemezulu, Elias Mabetoa, Sipho Sibanda, George Johannes Chirima

Grasslands represent one of the most extensive terrestrial biomes globally, covering approximately one-third of the Earth’s land surface, yet they are increasingly threatened by land-use change and overgrazing, underscoring the need for reliable monitoring approaches. This study…Read more

Assessing Maize Yield Spatiotemporal Variability Using Unmanned Aerial Vehicles and Machine Learning

Colette de Villiers, Zinhle Mashaba-Munghemezulu, Cilence Munghemezulu, George J. Chirima, Solomon G. Tesfamichael

Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanne…Read more

Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties

Lwandile Nduku, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, Wonga Masiza, Phathutshedzo Eugene Ratshiedana, Ahmed Mukalazi Kalumba, et al.

Monitoring crop growth conditions during the growing season provides information on available soil nutrients and crop health status, which are important for agricultural management practices. Crop growth frequently varies due to site-specific climate and farm management practices…Read more

Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms

Lwandile Nduku, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, Wonga Masiza, Phathutshedzo Eugene Ratshiedana, Ahmed Mukalazi Kalumba, et al.