Machine learning for agri-food processes: learning from data, human knowledge and interactions
Abstract
This chapter presents three examples of data-based machine learning on time series. The common denominator of these case studies is the sparseness of data, making machine learning results fragile and inaccurate. We show how human expertise can be effectively mobilized for building useful systems, for instance useful decision support systems, able to better meet the needs of the agri-food chain. The design and analysis of different features of machine learning coupled with human knowledge enables us to sketch future human-centered machine learning systems. This approach is very relevant for the modeling of agri-food systems, because human expertise, skills and know-how are rich and numerous, but often implicit, data are heterogeneous-big and sparse-and processes are complex and deeply conditioned by human needs and interactions.
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