AI-enabled Barilai-Borwein-Blinder-Oaxaca-Bernoulli Deep Classifier for Enhanced Crop Yield Prediction

This article explores the integration of advanced Artificial Intelligence (AI) enabled deep learning methods with accurate crop yield prediction. The objective of the work is to enhance the accuracy, sensitivity, and specificity of crop yield prediction. Also, false positive and false negative cases are minimized in crop yield prediction. AI-enabled Barilai-Blinder-Oaxaca-Bernoulli Deep Classifier (BBO-BDC) is proposed including preprocessing, feature selection, and crop yield prediction. First, the raw samples were collected from the crop yield prediction dataset. Barilai-Borwein Gradient Min-max Normalization-based preprocessing is applied to eliminate all missing values. Second, to provide fine-grained feature subsets, the Blinder-Oaxaca Statistical Decomposition-based feature selection method is used. Finally, an AI-enabled Bernoulli Deep Belief Network is designed to predict the crop yield. The empirical results demonstrate that the BBO-BDC technique significantly improves the accuracy up to 12%, specificity up to 15%, and sensitivity up to 3% with feature selection. Furthermore, the BBO-BDC technique realizes a substantial reduction in convergence speed by 29% and 51% reduction in overhead compared to conventional methods with feature selection. The study of innovative AI method integrated with Min-max Normalization, Barilai-Borwein gradient, Blinder-Oaxaca decomposition function, Deep Belief Network, Xavier Initialization function, Bernoulli distribution function, and Principal Components for achieving better performance in crop yield prediction.