THE ULTIMATE SMART FARMING APP

Abstract

Agriculture plays a vital role in economic development, and accurate crop prediction is essential for improving productivity and profitability. This project proposes a smart farming application, AgriGenius, which utilizes machine learning and deep learning techniques to recommend suitable crops based on soil nutrients and weather conditions. The system uses algorithms such as Random Forest for crop prediction and Long Short-Term Memory (LSTM) for predicting crop prices and yield trends. The application allows users to input parameters such as Nitrogen, Phosphorus, Potassium, soil type, and weather conditions to receive crop recommendations along with predicted yield and market prices. Additionally, the system provides information about seed purchase locations, helping farmers access required resources بسهولة. The system is implemented using Python, MySQL, and web technologies, enabling real-time interaction through a user-friendly interface. Experimental results demonstrate that the system provides accurate recommendations, helping farmers make informed decisions. This approach improves agricultural productivity, reduces risk, and supports smart farming practices.