A Hybrid Model that Combines Machine Learning and Mechanistic Models for Useful Grass Growth Prediction

Recently, Machine Learning (ML) has been heralded as a panacea for modelling problems across many domains, including Smart Agriculture (SmartAg), often in opposition to traditional mechanistic models arising on decades of scientific discovery. However, mechanistic models are often successful in "real world" problem-domains where ML models encounter difficulties (e.g., where the distribution of test data is not the same as the training data, violating the so-called identical and independently distributed (i.i.d.) assumption). In this paper, we consider a specific case of this opposition between a mechanistic model of grass growth and a ML model using historical, farm measurements. In our analyses, we find that both types of model have respective strengths. The mechanical model can often handle out-of-distribution events better than ML model, but the ML model can often handle temporary fluctuations in event variables (e.g., changing climate factors). Hence, we propose a combined hybrid model that learns which model to use when predicting grass growth. We argue that this combined approach has several practical benefits in providing stable and accurate predictions under widely varying conditions such as never before seen temperature fluctuations.