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Study: Applying Machine Learning to Agricultural Data - Abstract: 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 wellfounded, 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.

Author: Robert J. McQueen, Management Systems, University of Waikato, Hamilton, New Zealand. Stephen R. Garner, Craig G. Nevill-Manning, Ian H. Witten. Computer Science, University of Waikato, Hamilton, New Zealand.

Keywords: agriculture, horticulture, machine, agricultural, data, information, new zealand, database, management, software