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

Rajesh Kumar Dhanaraj¹, Nithya Rekha Sivakumar², Firoz Khan³

¹Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India. sangeraje@gmail.com.

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Summary

This study introduces an advanced AI deep learning classifier for precise crop yield prediction, significantly improving accuracy and reducing errors. The novel method enhances prediction performance and efficiency compared to traditional approaches.

Keywords:

Artificial intelligence Barilai–Borwein gradient Bernoulli Deep Belief Network Blinder–Oaxaca Statistical Decomposition Min–max Normalization

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Area of Science:

Agricultural Science
Computer Science
Data Science

Background:

Accurate crop yield prediction is crucial for food security and agricultural planning.
Existing methods often struggle with accuracy, sensitivity, and specificity, leading to false positives and negatives.
Advanced Artificial Intelligence (AI) and deep learning offer potential solutions for enhancing prediction capabilities.

Purpose of the Study:

To integrate advanced AI deep learning methods for accurate crop yield prediction.
To enhance the accuracy, sensitivity, and specificity of crop yield prediction models.
To minimize false positive and false negative cases in crop yield prediction.

Main Methods:

A novel AI-enabled Barilai-Blinder-Oaxaca-Bernoulli Deep Classifier (BBO-BDC) was developed.
Preprocessing involved Barilai-Borwein Gradient Min-max Normalization to handle missing values.
Feature selection utilized the Blinder-Oaxaca Statistical Decomposition method.
Crop yield prediction was performed using an AI-enabled Bernoulli Deep Belief Network.

Main Results:

The BBO-BDC technique improved accuracy by up to 12%, specificity by up to 15%, and sensitivity by up to 3%.
A significant reduction in convergence speed (29%) and overhead (51%) was achieved compared to conventional methods.
The integration of various AI components demonstrated superior performance in crop yield prediction.

Conclusions:

The proposed BBO-BDC technique offers a robust and efficient solution for accurate crop yield prediction.
AI-enabled deep learning methods, combined with advanced preprocessing and feature selection, significantly enhance prediction performance.
This approach holds promise for improving agricultural management and decision-making through precise yield forecasting.