Precision Agriculture based on Machine Learning and Remote Sensing Techniques

Abstract

In today's rapidly evolving agricultural landscape, the integration of precision techniques and data-driven approaches has become essential, driven by technological innovations, such as the Internet of Things (IoT), Artificial Intelligence (AI), and cutting-edge aerial and satellite technologies. Precision agriculture aims to maximize productivity by closely monitoring soil health and employing advanced machine learning methods for precise data analysis. This study explores the evaluation of soil quality, placing particular emphasis on leveraging remote sensing technology to collect comprehensive data and imagery to analyze soil conditions related to olive cultivation. By harnessing cloud platforms integrated with satellite data, several analytical tools are made available, offering valuable insights for informed decision-making and operational efficiency across various sectors. Furthermore, this study introduces an AI-driven application tailored to predict the soil moisture levels. This application facilitates in-depth analysis, feature extraction, and the prediction of different vegetation indices using time-series satellite imagery. The study's findings highlight the exceptional accuracy achieved by the decision tree and extra tree regression models, with soil moisture estimation reaching approximately 91%, underscoring the importance and effectiveness of the proposed method in advancing agricultural practices.