Vawda, M.I.; Lottering, R.; Mutanga, O.; Peerbhay, K.; Sibanda, M.
Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass. Sustainability 2024, 16, 1051.
https://doi.org/10.3390/su16031051
AMA Style
Vawda MI, Lottering R, Mutanga O, Peerbhay K, Sibanda M.
Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass. Sustainability. 2024; 16(3):1051.
https://doi.org/10.3390/su16031051
Chicago/Turabian Style
Vawda, Mohamed Ismail, Romano Lottering, Onisimo Mutanga, Kabir Peerbhay, and Mbulisi Sibanda.
2024. "Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass" Sustainability 16, no. 3: 1051.
https://doi.org/10.3390/su16031051
APA Style
Vawda, M. I., Lottering, R., Mutanga, O., Peerbhay, K., & Sibanda, M.
(2024). Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass. Sustainability, 16(3), 1051.
https://doi.org/10.3390/su16031051