• Akash Umesh Patil
• Omkar Chandrakant Patil
• Kishor Krishnat Shahapure
• Vishwanath rajaram Nigade

This abstract presents a comprehensive solution for enhancing agricultural practices through the integration of modern technologies, focusing on both crop yield optimization and price prediction. The Crop Yield and Price Recommendation System (CYPRS) utilizes data-driven approaches and machine learning (ML) algorithms to assist farmers and stakeholders in making informed decisions. The system processes diverse data sources, including historical climate data, soil information, crop-specific growth models, real-time monitoring, and market trends. By leveraging ML techniques such as linear regression, decision trees, and support vector machines, CYPRS provides personalized recommendations for optimizing crop yield, including ideal planting times, suitable crop varieties, irrigation schedules, and fertilizer application rates. Additionally, the system predicts future crop prices by analyzing factors such as historical crop prices, weather patterns, socio-economic indicators, and market trends. The user-friendly interface, built using React.js, ensures accessibility for farmers, traders, and policymakers, empowering them to optimize resource allocation, minimize risks, and enhance profitability. The CYPRS has the potential to significantly contribute to global food security, sustainable agricultural practices, and economic resilience in the agricultural sector.

Predicting prices and recommendations of crops

"KrushiMitra Ensuring Prices and Recommendations of Crops.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.f42-f47, October-2024, Available :http://www.jetir.org/papers/JETIR2410505.pdf

"KrushiMitra Ensuring Prices and Recommendations of Crops.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppf42-f47, October-2024, Available at : http://www.jetir.org/papers/JETIR2410505.pdf