AI-Driven Agricultural Forecasting Models for Semi-Arid Regions of Gujarat

Abstract

The semi-arid regions of Gujarat face significant challenges in agricultural productivity due to erratic rainfall, soil degradation, and climatic variability. This study explores the development and deployment of AI-driven agricultural forecasting models tailored for these regions. Using machine learning (ML) and deep learning techniques, the research aims to enhance crop yield predictions, optimize resource allocation, and support decision-making for farmers and policymakers. The research utilizes real-time meteorological, soil, and remote sensing data from the districts of Kutch, Banaskantha, and Surendranagar, demonstrating the efficacy of AI tools in promoting resilient and sustainable agriculture in water-scarce regions.