Definition of Zones With Different Levels of Productivity Within an Agricultural Field Using Fuzzy Modeling

Abstract

Zoning of agricultural fields is an important task for utilization of precision farming technology. A method based on fuzzy indicator model theory for definition of zones with different levels of productivity is considered. Fuzzy indicator model for identification of zones with different levels of productivity is based on two general types of fuzzy indicators: the individual fuzzy indicator (IFI) type and the combined fuzzy indicator (CFI) type. IFIs are defined as a number in the range from 0 to 1, which reflect an expert concept and are modeled by an appropriate membership function. CFI is defined using fuzzy aggregated operations. The theoretical considerations are illustrated with this example based on data collected from a precision agriculture study in central Texas, USA. Soil samples were collected at different points, taking into account the actual longitude and latitude for each of these points. Because the experimental data (as in many cases) contained information only about a limited number of parameters, the calculations were restricted in this study. In this study, the parameters grain yield, total carbon (C), total nitrogen (N), and available phosphorus (P) were utilized. Using the author’s computer program, fuzzy indicators IFI/CFI was calculated for each zone separately. Utilizing results of calculations maps of zones with different levels of productivity were built

Zoning of an Agricultural Field Accounting for the Significances of Parameters Affecting Productivity

“…In this method, experts can define their own opinions about the significance of parameters determining productivity in numerical forms, or as linguistic propositions. It should be noted that with this developed method (Kurtener et al, 2011(Kurtener et al, , 2013 it is postulated that at within an agricultural field all zones with the different levels of productivity exist, but with different grades. The value of the grade is estimated with fuzzy indicators, which rang from zero to one.…”

“…In this case, the expert opinions are formulated as several alternatives, and an optimal alternative is defined using the tool for multi-attributive decision-making. A chart illustrating how the combination of the developed method (Kurtener et al, 2011(Kurtener et al, , 2013) with a tool for multi-attributive decision-making could work is shown in figure 1.…”

“…In recent years, methods for zoning of agricultural fields using a fuzzy indicator model have been developed (Kurtener et al, 2011(Kurtener et al, , 2013. In the framework of this method, the significance of various parameters affecting productivity is evaluated by an expert panel.…”

“…In this paper, a method for assessing the significance of various parameters affecting crop productivity is elaborated in combination with the existing methods of agricultural field zoning (Kurtener et al, 2011(Kurtener et al, , 2013. The theoretical considerations are illustrated using data collected from a precision agriculture study in central Texas, USA (Torbert et al, 2000).…”

“…One method is to use methods of data processing of crop yield monitoring and mapping alone (Yakushev et al, 2007). In second method, complex parameters, which include both topographically referenced yield data and soil characteristics, are used for zoning of agricultural fields (Fridgena, 2004;Kurtener et al, 2011Kurtener et al, , 2013Kurtener, & Yakushev, 2014).…”