Precision Agriculture: Leveraging Sensor Technology for Soil Analysis to Recommend Crop Using Random Forest Approach

Abstract

In modern agriculture, leveraging  advanced technology can significantly enhance crop cultivation practices and optimize yields. This study proposes a comprehensive methodology for the development of a Crop suggestion system aimed at assisting farmers in making informed decisions regarding crop selection based on real-time sensor data analysis, machine learning techniques, and intuitive voice guidance. The objectives encompass sensor deployment for monitoring key parameters such as Nitrogen,  Phosphorus, and Potassium(NPK) levels, soil moisture, humidity, and temperature, followed by data collection, preprocessing and feature engineering to extract relevant insights from the collected data. Machine learning models are then developed and trained using historical sensor data to predict crop suitability for specific environmental conditions. A crop recommendation system is designed to integrate with real-time sensor data, providing tailored suggestions for optimal crop selection based on current environmental conditions. A user-friendly interface is developed, incorporating voice guidance capabilities to facilitate seamless interaction with the system. By following this methodology, farmers can make data-driven decisions to enhance agricultural productivity and sustainability, ultimately leading to improved crop yields and economic outcomes.