Agriculture remains a vital pillar of India’s economy, with a substantial portion of the population relying on farming as their primary livelihood. Despite its importance, many farmers continue to face barriers in maximizing crop productivity and maintaining soil health due to limited access to scientific guidance and data-driven tools. To address these challenges, this study presents an AI-based Decision Support System (DSS) designed to deliver personalized, real-time agricultural recommendations. The system encompasses three key modules: a Crop Recommendation Model, a Fertilizer Recommendation Model, and an interactive bilingual Chatbot supporting both Telugu and English. The crop recommendation module identifies optimal crops based on soil nutrient profiles and environmental parameters, while the fertilizer module suggests suitable nutrient combinations for sustainable and efficient soil management. The integrated chatbot functions as a virtual assistant, providing user-friendly support and addressing common queries in local languages, thereby enhancing accessibility for rural farmers.

Powered by machine learning algorithms, the system processes large-scale agricultural datasets to generate context-aware insights that adapt to dynamic environmental conditions and user-specific inputs. This adaptive platform aims to support informed decision-making, improve agricultural efficiency, and encourage sustainable practices, particularly among smallholder and marginal farmers. By integrating artificial intelligence with localized agricultural knowledge, the proposed system offers a scalable solution for enhancing farm productivity, profitability, and rural resilience. The initiative aligns with broader objectives of sustainable agriculture, food security, and socio-economic development in agrarian communities.