AIGEN-CROP-DS: AI-Driven Bio-Mathematical Framework for Crop Genetic Disease Prediction Using Multimodal Datasets

Adlin Jeba Kumari
Research Scholar, Department of Computer Science and Engineering, BGSIT, Adichunchanagiri University, Bangalore, Karnataka, India

A. Jayanthiladevi
Professor, Department of Computer Science and Engineering, BGSIT, Adichunchanagiri University, Bangalore, Karnataka, India

Suresh Kumar Sahani
Faculty of Science, Technology, and Engineering, Rajarshi Janak University, Rajarshi Janak University, Janakpurdham, Nepal

Abstract

The backbone of global food security is crops rich in carbohydrates, such as wheat, maize, and rice, combined with protein sources like quinoa and lentils, and oil crops such as canola and sunflower. GDPM focuses on varieties resistant to rust or smut and with high starch or fiber content. This study proposes AIGEN-CROP, a bio-mathematical and artificial intelligence-driven framework that integrates computational image analysis, nutritional mapping, and disease-resistance prediction to assess genetic disorders in crops. 

Keywords: Crop Disease, Genetic Disease, Prediction, Optimization, Machine Learning