Fuzzy Classification in the Determination of Input Application Zones

Abstract

Correctly interpreting soil fertility and its spatial distribution within an area helps to lessen losses and environmental effects associated with agriculture, to optimize fertilization and liming practices. This study is aimed at using concepts and methods from spatial and temporal analyses to soil fertility and to develop a fuzzy classification methodology in an effort to define input application zones in three conilon coffee harvests. An irregular network with georeferenced points was built in the central region of the farm. Soil samples were collected at 0.00-0.20 m depth within the projections of tree canopies. Geostatistical analysis was used to set up maps in which the variables were shown. In such maps, input and output fuzzy sets were created and applied, as well as rules of inference and determination of to-be-applied logical operators. Fuzzy classification of the area was performed in the three harvests so as to define whether or not inputs were needed. Our main findings show that the N-P-K requirement was spatially dependent in all harvests. By classifying the area using fuzzy logic, it was possible to analyze soil fertility and to indicate the regions having the smallest and greatest needs for N-P-K and liming.

Fuzzy Logic in the Spatial and Temporal Distribution in the Quality of the Beverage in Conilon Coffee

“…The nugget effect (C0) represents the unexplained variability and according to Lima et al (2016), the lower the proportion of the nugget effect in relation to the semivariogram level, the greater the spatial dependence presented, which in this study presented GDE equal to 38.0% and 33.0% for harvests 1 and 2, respectively. This spatial dependence is considered moderate according to the classification of (CAMBARDELLA et al, 1994).…”

Fuzzy Logic and Cluster Analysis in the Quality of the Beverage From Conilon Coffee

“…As a result, fuzzy logic has been used as a tool in decisionmaking (LANZILLOTTI;LANZILLOTTI, 1999;LAZIM;SURIANI, 2009;CAVALCANTI et al, 2013;CHOROBURA;CASTANHO;TEIXEIRA, 2016). This method aims to solve problems where information is not well defined, and is based on modelling such problems, translating into mathematical terms the imprecise information contained in the natural language expressed by linguistic variables that can be transformed (LIMA et al, 2016;SENTÜRK, 2017).…”