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ARS Home » Southeast Area » Auburn, Alabama » Soil Dynamics Research » Research » Publications at this Location » Publication #261488

Research Project: Using Agricultural and Industrial Byproducts to Improve Crop Production Systems and Environment Quality

Location: Soil Dynamics Research

Title: Zoning of agricultural field using a fuzzy indicators model

Author
item Kurtener, D - Russian Academy Of Sciences
item Yakushev, V - Russian Academy Of Sciences
item Torbert, Henry - Allen
item Kruger, E - Russian Academy Of Sciences

Submitted to: European Conference on Precision Agriculture Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 4/2/2011
Publication Date: 7/11/2011
Citation: Kurtener, D., Yakushev, V., Torbert III, H.A., Kruger, E. 2011. Zoning of agricultural field using a fuzzy indicators model. In: Proceedings of the European Conference on Precision Agriculture, July 11-14, 2011, Prague, Czech Republic. CDROM.

Interpretive Summary: Zoning of agricultural fields is an important task for utilization of precision farming technology. One method for deciding how to subdivide a field into a few relatively homogenous zones is using applications of fuzzy sets theory. Data collected from a precision agriculture study in central Texas, USA, was utilized as the test area for studying fuzzy set therory in precision agriculture. Fuzzy set techniques were used to outline zones with different levels of productivity using a fuzzy indicators model. The theoretical considerations are illustrated within this manuscript.

Technical Abstract: Zoning of agricultural fields is an important task for utilization of precision farming technology. One method for deciding how to subdivide a field into a few relatively homogenous zones is using applications of fuzzy sets theory. Data collected from a precision agriculture study in central Texas, USA, was utilized as the test area for studying fuzzy set therory in precision agriculture. Fuzzy set techniques were used to outline zones with different levels of productivity using a fuzzy indicators model. The theoretical considerations are illustrated within this manuscript.