Combining expert knowledge with machine learning on the basis of fuzzy training

•We introduced a fuzzy training approach based on nonlinear regularization.•The aim is to find a compromise between expert knowledge and training.•An open source implementation in Python/Cython is available.•The fuzzy training was verified using more than 4500 sugar beet yield record.

The paper introduces a fuzzy training approach based on nonlinear regularization in an effort to avoid over training. The main idea is to restrict training so that the basic expert knowledge used to build the model is still visible. This is implemented by a new nonlinear regularization approach which can be applied to any kind of training data set. The approach is demonstrated using a large crop yield data set (>4500 field records) for sugar beet collected in agricultural farms over a 14-year period (1976-1989) in East Germany. The software is implemented in SAMT2, free and open source software, using the Python programming language.