Improving Conflict Resolution in Version Spaces for Precision Agriculture

AUTHOR(S):

TITLE

Improving Conflict Resolution in Version Spaces for Precision Agriculture

ABSTRACT

We developed a plant monitoring system that uses machine learning to classify environment conditions as favorable or not for plant development. The decision is taken based on six features whose values are measured from sensors: light, temperature, vibrations, soil humidity, rain quantity and vertical distance. Aiming to assure transparency in the classification decision, we used a modified version of the version space algorithm. We adapted the version space algorithm to deal with situations when hypotheses do not agree on a single decision. As a result, 20% from the instances unclassified by the version space were classified by our enhanced version space algorithm. The developed tool is available online as an open-source project.

KEYWORDS

Precision agriculture, Knowledge in learning, Version space algorithm, Sensor-based reasoning

REFERENCES

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Cite this paper

Adrian Groza, Iulia Ungur. (2018) Improving Conflict Resolution in Version Spaces for Precision Agriculture. International Journal of Agricultural Science, 3, 26-33