Intelligent Agriculture Platform for Precision Farming

Abstract: Agriculture plays a vital role in the economic development of many countries, especially India, where a significant portion of the population depends on farming for their livelihood. Despite this importance, many farmers still rely on traditional methods and personal experience to decide which crops to grow. These methods often ignore critical factors such as soil nutrient levels, weather conditions, and historical crop performance, which can lead to poor yield, financial losses, and inefficient use of land. With the rapid growth of Artificial Intelligence and Machine Learning, there is a strong opportunity to improve agricultural decision-making through data-driven systems. This project presents an AI-Based Smart Crop Recommendation and Yield Prediction System developed using Machine Learning and web technologies. The primary objective of the system is to recommend the most suitable crops for cultivation based on soil characteristics, weather parameters, and farm-related inputs. The system also predicts the expected crop yield, helping farmers plan their resources and investments more effectively. A Random Forest machine learning algorithm is used to analyze agricultural datasets and generate accurate crop recommendations. The model provides the top three crop suggestions along with model accuracy, improving reliability and transparency. The application is implemented as a Flask-based web platform with secure user authentication. Farmers can register, log in, manage their farm profiles, view crop recommendations, analyze soil health, review weather history, and predict crop yield. An admin module is included to allow dataset upload and model retraining, ensuring that the system can adapt to new data over time. The system uses a SQLite database for efficient data storage and retrieval. Overall, this project demonstrates how artificial intelligence can be effectively applied in agriculture to support informed decision- making, increase productivity, and promote sustainable farming practices.

Keywords: Agriculture, Machine Learning, Crop Recommendation, Yield Prediction, Random Forest, Precision Farming, Flask Web App