Applying machine learning to agricultural data

• Published in: Computers and electronics in agriculture 1995-06, Vol.12 (4), p.275-293
• Main Authors: McQueen, Robert J., Garner, Stephen R., Nevill-Manning, Craig G., Witten, Ian H.
• Format: Article
• Language: English
• Subjects: Agronomy. Soil science and plant productions; Animal productions; Biological and medical sciences; Biometrics, statistics, experimental designs, modeling, agricultural computer applications; Culling management; Exact sciences and technology; Expert systems; Fundamental and applied biological sciences. Psychology; General aspects; Generalities. Biometrics, experimentation. Remote sensing; Information and communication sciences; Information science. Documentation; Knowledge systems; Machine learning; Sciences and techniques of general use; Terrestrial animal productions; Vertebrates
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/0168-1699(95)98601-9

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.