An analysis the Enhancing agricultural Decision-making with a Hybrid Crop Selection Algorithm (CSA)

Agricultural productivity is highly dependent on the selection of suitable crops based on various environmental, soil, and economic factors. Traditional crop selection methods often lack adaptability and fail to integrate multi-dimensional data for optimized decision-making. This study suggests an algorithm for crop selection. This method uses a number of models to estimate both the level of groundwater & prices of agricultural crops. the strengths of both the linear ARIMA method & nonlinear ANN model, a hybrid model is clearly required to assess the accuracy of price forecasts. We attempted to develop a hybrid technique using multi-classifiers, as single-classifiers were previously the exclusive tool for prediction & forecasting in the agricultural domain.