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Applying machine learning to agricultural data
1995
Computers and Electronics in Agriculture
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
doi:10.1016/0168-1699(95)98601-9
fatcat:tr4ayqfa7jfqbjvozpeenm3svu