The Empirical Analysis For Effective Prediction of Crop Price Using Neuro Evolutionary Algorithm based on Machine Learning Approach

Abstract

Abstract : Based on a neuro evolutionary algorithm, this study proposes a neural network model for efficient crop price prediction. When viewed from the perspective of agricultural business, the market price of corn reflects its current demand. For agricultural management, to improve profits, it is important to track and predict the market price. In this manuscript, a neural network model based on the output prediction of a neuro evolutionary algorithm is used to predict the price of the crop and compared with the existing Naive Bayes algorithm. By recognizing patterns in our training dataset, which serves as one of the inputs to the algorithm, we are able to determine the price of the crop. The parameters (Yield, Rainfall, Minimum Support Price, and Maximum Trade ) are fed to the algorithm by the user. It is compared with existing algorithms to determine the performance of the proposed algorithm. The features considered for the analysis are Climate, historical costs, location, demand indicators, and crop health are the variables used for predicting future corn crop cost. According to the empirical analysis, the proposed model is significantly more accurate in predicting crop prices.