.png){kind=icon}
.png){kind=icon}
Return
APPLYING MACHINE LEARNING TO AGRICULTURAL DATA
.png){kind=icon}
OA
AI
DOI：10.1016/0168-1699(95)98601-9.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
MACHINE LEARNING
KNOWLEDGE SYSTEMS
CULLING MANAGEMENT
DATABASES
EXPERT SYSTEMS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
10.0K
Citations:
4.8W
Organization
No organization information available