Enhanced Elman Spike neural network optimized with Red Fox optimization algorithm for sugarcane yield grade prediction

Abstract

ABSTRACT In this manuscript, Enhanced Elman Spike Neural Network (EESNN) optimized with Red Fox optimization algorithm is proposed for Sugarcane Yield Grade Prediction (SYGD-EESNN-RFOA). Initially, the sugar yield prediction dataset is taken. Then the input data are pre-processed by hybrid decomposition method that is morphological filtering and extended empirical wavelet transformation (MF-EEWT) to retrieve the missing values. These pre-processed outputs are given to feature selection methods. During the process of feature selection, Entropy – Kurtosis-based feature selection method (EKBFS) is applied. These extracted features are fed to EESNN, and then it classifies the sugarcane yield as low grade, medium grade, and high grade. Generally, EESNN method does not indicate the use of any optimization strategies for calculating the best parameters to ensure accurate sugarcane yield forecast. To forecast the sugarcane production accurately, the Red Fox Optimization Algorithm (RFOA) is proposed. The proposed approach is carried out in Python; its performance is evaluated under performance metrics, such as precision, root mean square error, mean square error, mean absolute percentage error, convergence curve, and predicted percentage of changes in sugarcane yield during 2021–2027. The proposed SYGP-EESNN-RFOA framework attains higher accuracy of 27.5%, 16.65%, and 9.13%, 15.21% higher specificity compared with the existing methods.