Machine Learning Enabled IoT System for Agricultural Land Recommendation

Abstract

This study aims to develop an agricultural land recommendation system by integrating the Internet of Things (IoT) and machine learning (ML). IoT devices, including the JXBS-3001 soil sensor and Raspberry Pi Pico RP2040, collect real-time soil data, which is analyzed using the decision tree (DT) algorithm. The DT algorithm is chosen for its simplicity, efficiency, and interpretability over random forest (RF) and k-nearest neighbors (k-NN). It provides structured decision-making, faster training, and better handling of numerical data for parameters such as soil pH, nutrient content (NPK), moisture levels, and temperature. The findings show that the system provides accurate crop recommendations, helping farmers make informed decisions. The integration of IoT and ML enhances land assessment and optimizes agricultural productivity. Future improvements could include weather analysis and plant disease detection to further support decision-making.