FutureCrop: AI based smart agriculture decision support system

Agriculture remains a crucial sector for economic growth and food security; however, farmers often face challenges such as uncertain market prices and improper crop selection. These challenges lead to financial losses and inefficient utilization of resources. To overcome these issues, this project proposes FutureCrop, an intelligent machine learning based system for crop recommendation and food crop price prediction. The project will recommend suitable crops by analyzing soil nutrients such as Nitrogen, Phosphorus, Potassium, and pH, along with climatic factors including temperature, humidity, and rainfall using a Random Forest classifier. Additionally, future prices of food crops will be predicted by examining historical data using Long Short-Term Memory (LSTM) based time-series forecasting model. This system will be developed using Python and deployed through a web-based interface to ensure usability and accessibility for farmers and agricultural stakeholders. By integrating crop recommendation and price prediction into a single platform, the FutureCrop system is expected to assist farmers in selecting profitable crops and identifying optimal selling times. This approach aims to reduce farming risks, enhance productivity, and support data-driven decision-making in modern agriculture.