Paper Contents
Abstract
The agricultural sector faces unprecedented challenges including climate variability, resource constraints, and the need to meet growing food demands. Traditional farming practices often fall short in addressing these complexities, necessitating data-driven, intelligent solutions. This literature survey provides a comprehensive review of state-of-the-art approaches in AI-driven agricultural decision support systems, specifically examining crop yield prediction, crop recommendation systems, disease detection, fertilizer recommendation, crop rotation planning, and market price forecasting. Drawing from recent research publications (2018-2025), this paper analyzes existing methodologies, identifies their strengths and limitations, and proposes pathways for developing AgroMindan integrated AI-powered agricultural decision support system that leverages machine learning, deep learning, generative AI, and explainable AI to empower farmers with comprehensive, multilingual agricultural guidance.Keywords: Precision Agriculture, Crop Yield Prediction, Crop Recommendation Systems, Disease Detection, Explainable AI, Generative AI, Market Price Forecasting, Sustainable Agriculture
Copyright
Copyright © 2025 Abhijit Manohar Shinde. This is an open access article distributed under the Creative Commons Attribution License.
