Agriculture plays a critical role in food security and economic development. However, plant diseases, improper growth conditions, and lack of timely guidance significantly affect crop productivity. Early identification of plant health issues can reduce losses and improve yield quality. This paper presents AgroSense AI, an intelligent plant health advisory system that utilizes image-based analysis and environmental data to assist users in monitoring plant health. The proposed system identifies plants using images captured through a camera or uploaded by the user, provides growth and nutrient recommendations, simulates plant growth through an educational interface, and offers weather-based advisory support. The system is implemented using Python and Streamlit, integrating image processing techniques and external APIs. The proposed solution is cost-effective, user-friendly, and suitable for academic and educational agricultural applications..
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