Binary Classification of Agricultural Crops Using Sentinel Satellite Data and Machine Learning Techniques

The automated process of determining the crop type carried on plots of land, leveraging data provided by earth observation satellites, represents a highly valuable ability that can serve as a foundation for subsequent analyses or as input for calibrating models, such as Decision Support Systems. This paper presents a study on the task of crop classification starting from indices derived from imagery data provided by ESA Satellites Sentinel 1 and 2. We create a valuable tool to verify farmers' claims, especially in relation to state subsidies for specific crops of interest. To this purpose, we focus on perfecting a binary classification for each of five crops of interest (Tomatoes, Soy, Sugar Beet, Rice, and Wheat), aimed to accurately discern the target crop against any other possible crop. The paper investigates various preprocessing techniques to create a dataset suitable for traditional machine learning methods, which presumes that each land plot to classify is represented by a fixed set of features. To deal with inevitable missing observations caused by clouds or other environmental factors, we investigate different imputation strategies (linear interpolation and constant value filling). Complementary, we study the impact of imbalanced classification labels and evaluate the effectiveness of standard balancing techniques. The findings offer practical implications for monitoring and optimizing agricultural practices in the context of precision farming and sustainable agriculture.