Crop Type Prediction Across Countries and Years: Slovenia, Denmark and the Netherlands - Merdjanovska, Elena; Kitanovski, Ivan; Kokalj, Žiga; Dimitrovski, Ivica; Kocev, Dragi (IEEE, 2022)
Crop type prediction is a very relevant and a very challenging task. The increasing availability of high-quality satellite imagery and machine learning have enabled the development of automatic crop type classification methods. In this paper, we present a crop type prediction data suite that consists of crop type information from three countries (Denmark, the Netherlands, and Slovenia) across three years (2017, 2018 and 2019). By considering the complex challenges contained by this data suite, we investigate the robustness of 7 deep learning methods used for crop type prediction (TempCNN, MSResNet, InceptionTime, OmniscaleCNN, LSTM, StarRNN, and Transformer networks). The comprehensive experiments reveal that the recurrence-based methods perform the best (with LSTM being the best performing). The methods can achieve very good predictive performance - up to a weighted F1 score of 0.8432.
Keywords: Crop Type Prediction , Deep Learning , Time series classification , Benchmarking of performance