Design and Developing AI-Driven Agro-sage for Optimal Precision Agriculture

This research work introduces an AI-driven solution for monitoring and optimizing soil health through the detection of soil nutrients such as nitrogen, phosphorus, potassium, and moisture content. By utilizing advanced sensor technologies and machine learning algorithms, the system provides real-time analysis and insights into soil conditions. The data collected is processed to offer tailored recommendations for nutrient improvement, including both organic and inorganic methods. The AI model integrates various environmental and soil parameters to predict nutrient deficiencies and suggest appropriate interventions. A user-friendly mobile application serves as the interface, allowing farmers and gardeners to access and act on these insights easily. The app provides detailed reports and actionable advice, enabling users to implement effective soil management practices. This innovative approach aims to enhance agricultural productivity and sustainability by promoting precise and informed nutrient management strategies.