Smart Recommendation System For Flourishing Gardens Using Deep Learning Techniques

Our project introduces a specialized model focusing on companion planting, specifically for the onion-carrot pair. It integrates advanced deep learning algorithms, including graph neural networks (GNNs), collaborative filtering, and content-based filtering. The model offers personalized companion plant recommendations based on user input and incorporates pest control and disease prediction functionalities tailored to selected plants. Through thorough data analysis and consideration of user preferences, the model provides customized suggestions to optimize plant growth and health, taking into account environmental factors such as climate conditions. Utilizing GNNs, the model enhances pest control by analyzing images to identify pests, while collaborative filtering refines pest management strategies based on similar contexts. For disease prediction, content-based filtering evaluates historical data and plant traits to forecast disease risks, facilitating proactive measures for plant health protection. By harnessing advanced deep learning techniques and tailored recommendations, this project aims to empower agricultural practitioners with actionable insights for sustainable gardening practices, enhancing crop productivity, and promoting environmental stewardship in agriculture.