Developing Sorghum Forecasting Models using Artificial Intelligence

This abstract include the other forecasting models.

Sorghum Yield Forecasting Model Using Satellite based Vegetation Indices in the Rain fed Sectors in Sud.

Nine Neural Network forecasting models are investigated in one study area ( Gedaref) GRVI is [Green Red Vegetation Index] used for Neural Network input Preprocessing by normalizing the data sets with mean 0 and standard deviation 1 • Sorghum forecasting models are developed using RBF [Radial Basis Function] network.

Developing Sorghum Forecasting Models using Artificial Intelligence

Artificial Neural Network (ANN) architectures are developed, investigated, and tested for forecasting Sorghum yield for the selected study area in Gedarif state. Rainfall data is provided by Meteorological Authority, Satellite images are collected from (Famine Early Warning System (FEWS) website, and Sorghum yield is provided by Ministry of Agriculture – Gedarif state. The study has developed and analyzed 32 models of neural networks to predict the production of Sorghum yield (as output data) by using the seasonal rainfall data and satellite images vegetation indices (as input data).