PRECISION AGRICULTURE BASED ON MACHINE LEARNING AND REMOTE SENSING TECHNIQUES

Abstract

In today’s agricultural landscape, precision is crucial, utilizing advanced technologies like IoT, AI, aircraft, and satellite systems. Smart agriculture aims to revolutionize production by monitoring soil quality and employing data analytics through machine learning. This study enhances soil quality assessment in Saudi Arabia’s Aljouf region using remote sensing technology. Leveraging UAVs, remote sensing provides crucial data and imagery for olive cultivation. Cloud platforms with satellite data offer invaluable information, aiding public and private decision-making. Proposing an AI-powered application combined with remote sensing, this paper develops predictive models for soil moisture and time-series satellite image analysis. Experimental results show a 91% accuracy with decision tree and extra trees regressor models, highlighting their effectiveness. This research transforms agricultural productivity through advanced technology and data-driven methodologies.