There is a growing need for remote identification of crop types, which is a serious issue for policymakers and statistical accountants (i.e., agricultural inspectors and government agencies), for verifying the degree of validity of the information concerning the area and the type of each crop being cultivated. In this work, remote Sentinel-2 imaging data was utilized for calculating average NDVI values, twice a month, for the period 2017–2020, for cotton, rice and olive trees. In addition, a machine learning algorithm was developed and the corresponding model was trained using the average NDVI values. Python programming and KNN machine learning on the PyCharm environment were used.