Authors - Prasad Chaudhari, Ritesh V. Patil, Parikshit N. Mahalle Abstract - The Agricultural Productivity Enhancement System leverages data-driven pattern classification and machine learning-based fertility detection to improve farming efficiency. The architecture integrates IoT sensors, satellite imagery, and soil analysis to collect crucial agricultural data. A preprocessing module ensures data cleaning and feature extraction, storing refined data in an agricultural repository for further analysis. Machine learning models, including pattern classification and fertility detection, process this data to assess crop health and soil fertility. A decision support system then provides real-time recommendations to farmers, enhancing precision agriculture. Researchers and data analysts contribute to model refinement, ensuring scalability and adaptability. This system optimizes resource allocation, reduces wastage, and increases crop yield by enabling real-time, AI-driven decision-making.
