APPLYING MACHINE LEARNING TO AGRICULTURAL DATA

Many techniques have been developed for learning rules and relationships automatically from diverse data sets, to simplify the often tedious and error-prone process of acquiring knowledge from empirical data. While these techniques are plausible, theoretically well-founded, and perform well on more or less artificial test data sets, they depend on their ability to make sense of real-world data. This paper describes a project that is applying a range of machine learning strategies to problems in agriculture and horticulture. We briefly survey some of the techniques emerging from machine learning research, describe a software workbench for experimenting with a variety of techniques on real-world data sets, and describe a case study of dairy herd management in which culling rules were inferred from a medium-sized database of herd information.