Developing an Optimal Model for Predicting the Severity of Wheat Stem Rust (Case study of Arsi and Bale Zone)

arXiv:2402.10492v1 Announce Type: new
Abstract: This research utilized three types of artificial neural network (ANN) methodologies, namely Backpropagation Neural Network (BPNN) with varied training, transfer, divide, and learning functions; Radial Basis Function Neural Network (RBFNN); and General Regression Neural Network (GRNN), to forecast the severity of stem rust. It considered parameters such as mean maximum temperature, mean minimum temperature, mean rainfall, mean average temperature, mean relative humidity, and different wheat varieties. The statistical analysis revealed that GRNN demonstrated effective predictive …

abstract ann artificial arxiv backpropagation case case study cs.ai cs.lg function functions general network neural network regression research rust stem study training transfer type types