Automatic Classification of Agricultural Summer Crops in Uruguay

Abstract

In this work, we present a study for the classification of summer crops on a nationwide perspective. Using both optical and radar satellite images, we implement a time-series classification algorithm using XGBoost. Two datasets with farm-level information were used: one with ground truth obtained directly from farmers production and the other with declared crops obtained at the government level. The crops analyzed were corn, soybean, sorghum, and pastures. When trained and validated with ground truth, the classifier yields a F1-Score performance of 99% for soybean, and values higher than 80% for corn and sorghum. Predictions performed with this model on the dataset of declared crops lead to F1-Score values of 54, 97, and 50%, for corn, soybean, and sorghum, respectively. These low values for corn and sorghum indicate the presence of mislabeled data in that dataset, which in turns may suggest issues with the declarations provided by the farmers.