Predicting crop rotations using process mining techniques and Markov principals

Meeting an increasing demand for food while preserving the environment is one of the most important challenges of the 21st century. To meet this challenge, conservation agriculture can rely on the age-old practice of crop rotation. The objective of this article is to develop a methodology for predicting and visualizing crop rotations, supporting discussions between agronomists and producers. Based on crop history data, the 6-phase methodology, uses Markov chains for the prediction of the N most likely crops grown in year n + 1. Process mining and Directly-Follows Graphs (DFG) enables modelling and visualization of the results. Generalisation and filtering operations highlight the frequent behaviors of producers. Applied to analyse the crop history of 10,376 fields from 409 field crop farms in Quebec, Canada, the methodology is competitive with the performance of various recurrent neural networks (LSTM, RNN, GRU) with a successful prediction rate that exceeds 90%, while allowing for an intelligibility of results and a relative computational simplicity.