TITLE

Improving Conflict Resolution in Version Spaces for Precision Agriculture

pdf PDF

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

[1] Stefan Coniu and Adrian Groza. Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning. Expert Systems with Applications, 64:269–286, 2016.

[2] Luc De Raedt and Stefan Kramer. The levelwise version space algorithm and its application to molecular fragment finding. Proceedings of the 17th International Joint Conference on Artificial Intelligence - Volume 2, pages 853–859, 2001.

[3] MOSBAH EL SGHAIR, RAKA JOVANOVIC, and MILAN TUBA. An algorithm for plant diseases detection based on color features.

[4] Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lotfi A. Zadeh. Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.

[5] H. Haym. Generalizing version spaces. Machine Learning, 17(1):5–46, 1994.

[6] T. P. Hong and S. S. Tseng. A generalized version space learning algorithm for noisy and uncertain data. IEEE Transactions on Knowledge and Data Engineering, 9(2):336–340, 1997.

[7] T.M. Mitchell. Version Spaces: An Approach to Concept Learning. Ph.D. thesis, Stanford University, 1978.

[8] Herbrich Ralf, Graepel Thore, and Williamson Robert C. The Structure of Version Space, pages 257–273. Springer Berlin Heidelberg, Berlin, Heidelberg, 2006.

[9] Michle Sebag. Delaying the choice of bias: A disjunctive version space approach. In Proceedings of the 13 th International Conference on Machine Learning, pages 444–452. Morgan Kaufmann, 1996.

[10] C. H. Wang, T. P. Hong, and S. S. Tseng. Inductive learning from fuzzy examples. Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1:13–18, 1996.

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

 

cc.png
Copyright © 2018 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0