Modeling of Chaotic Political Optimizer for Crop Yield Prediction

Abstract

Crop yield is an extremely difficult trait identified using many factors like genotype, environment and their interaction. Accurate Crop Yield Prediction (CYP) necessitates the basic understanding of the functional relativity among yields and the collaborative factor. Disclosing such connection requires both wide-ranging datasets and an efficient model. The CYP is important to accomplish irrigation scheduling and assessing labor necessities for reaping and storing. Predicting yield using various kinds of irrigation is effective for optimizing resources, but CYP is a difficult process owing to the existence of distinct factors. Recently, Deep Learning (DL) approaches offer solutions to complicated data like weather parameters, maturity groups, genotype, etc. In this aspect, this paper presents an Automated Crop Yield Prediction utilizing Chaotic Political Optimizer with Deep Learning (ACYP-CPODL) model. The proposed ACYP-CPODL technique involves different processes namely pre-processing, prediction and parameter optimization. In addition, the hybrid Convolutional Neural Network (CNN) Long-Short Term Memory (LSTM) technique is designed for the prediction process. Moreover, the hyperparameter tuning of the CNN-LSTM approach is performed by the CPO algorithm. The proposed ACYP-CPODL technique has produced an effective result with an MSE of 0.031 and R2 Score of 0.936, whereas the BLSTM model has produced a near-optimal results. As a result, the proposed ACYP-CPODL method has proven to be an effective tool for predicting the crop yields. For validating the improved predictive performance of the ACYP-CPODL technique, a wide range of simulations take place on benchmark datasets and the comparative results highlighted the betterment of the ACYP-CPODL technique over the recent methods.