SmartKrishi-AI: A Survey of AI-Integrated Agriculture Decision Support and Farm Resource Management

Agricultural choices depend heavily on a complex mix of environmental variables, including soil properties, fluctuating weather patterns, crop compatibility, market dynamics, pest threats, and the availability of vital farming resources. Most contemporary digital services target only one of these factors, forcing growers to manually gather data from multiple disconnected websites and local contacts. This paper reviews recent developments related to SmartKrishi–AI, a conceptual, integrated platform designed for agricultural decision-making and asset coordination. Our analysis evaluates several vital domains, including crop recommendation frameworks, weather forecasting networks, commodity price models, plant disease diagnostics, public welfare discovery, equipment leasing, and labor scheduling. We analyze recent research from 2023 to 2026 indexed across major venues such as IEEE, Elsevier/ScienceDirect, and MDPI, exploring their mathematical approaches, dataset constraints, evaluation methods, and practical challenges. Based on these insights, we introduce a conceptual multilayer architecture that connects data-driven analytical models with verified institutional channels and peer-to-peer resource exchange workflows. Because the proposed ecosystem serves as a structural blueprint rather than an experimentally fieldtested software tool, no project-specific crop yield or predictive precision increases are claimed. Finally, we highlight persistent open challenges in this sector, such as data quality gaps, geographic adaptation limits, model interpretability, forecast volatility, system interoperability, regional language support, and the necessity for rigorous field trials.