A Review: AI-Driven Smart Agriculture: An Integrated Approach for Soil Analysis, Irrigation and Crop-Fertilizer Recommendations.

AI-driven smart agriculture systems have the potential to transform traditional farming into an intelligent and sustainable agricultural ecosystem. The integration of Artificial Intelligence, Machine Learning, Deep Learning, IoT sensors, and Explainable AI can improve agricultural productivity and reduce resource wastage.   This review paper analyzed various research works related to soil analysis, irrigation management, crop recommendation, and fertilizer recommendation systems. The study found that advanced models such as Transformer-based learning models, TabNet, ensemble learning methods, and IoT-enabled frameworks provide highly accurate agricultural predictions. Despite significant progress, several challenges still exist, including high implementation cost, lack of scalability, insufficient explainability, and limited real-world deployment. Future research should focus on developing affordable, explainable, and region-independent smart agriculture systems that can support farmers in real-time decision-making.   The adoption of AI-based agriculture technologies can help improve food security, optimize water usage, maintain soil health, and promote environmentally sustainable farming practices.

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