Fuzzy Modeling of the Effects of Irrigation and Water Salinity in Harvest Point of Tomato Crop. Part I: Description of the Method

Abstract

It was used statistical techniques for the evaluation of agricultural experiments, but there are mathematical theories that allow finer adjustments, highlighting among them, the fuzzy logic. The objective of the study was characterizing a method of fuzzy modeling from an agronomic experiment. For this study it was used data from an experiment conducted at the School of Agriculture of São Paulo State University (UNESP) in Botucatu-SP. The system input variables based in fuzzy rules were soil water tension and doses of water salinity, being defined three fuzzy sets. The output variables was elected from the biometric and productivity analysis that showed statistically significant differences, namely, plant height, stem diameter, leaf area, green biomass, dry weight, number of fruits, average fruit weight and percentage of disabled fruits. For output variables 9 fuzzy sets were defined. From the adopted methodology, the model allowed extract directly from the data set a base of rules without the use of questionnaires to experts for its preparation. In addition, it will analyze intermediate regions at trial levels and weave other conclusions of the tomato growth and productivity, not limiting in this way only those observed with statistical analysis.

Technology 4.0 with 0.0 costs: fuzzy model of lettuce productivity with magnetized water

“…From the input data, the fuzzy rule-based system (FRBS) will determine the output values of the system by a set of rules based on a method that considers the percentiles of data sets, thus not requiring the knowledge of the experts (Figure 4). This method that relates the percentiles of data sets was adopted and used by Cremasco, Gabriel Filho, and Cataneo (2010), Gabriel Filho, Cremasco, Putti, and Chacur (2011), Gabriel Filho et al (2016), Putti, Kummer, Grassi Filho, Gabriel Filho, and Cremasco (2017b), Viais Neto et al (2019a2019b), andMartínez et al (2020). Table 3 provides the behavior of lettuce for different irrigation depths and water types, and all possibilities are considered.…”

Multicriteria Model of Support for the Selection of Pear Varieties in Raising Orchards in the Semberija Region (Bosnia and Herzegovina)

“…They chose the fuzzy TOPSIS (technique for order of preference by similarity to ideal solution) and AHP (analytic hierarchy process) and entropy methods. Viais Neto et al [23] followed fuzzy logic to examine the effect of selected variables on tomato growth and productivity. Berk et al [24] decided to use a fuzzy logic algorithm to select methods of spraying plants in orchards, and proved that an intelligent automated system uses 4.8 times less spraying mixture compared to the conventional approach.…”

Fuzzy Modeling in Orange Production Under Different Doses of Sewage Sludge and Wastewater

“…Such an expression is referred to as the form of the rule based on cause and consequence. The rule base of the fuzzy model proposed was developed with a methodology similar to that used by Cremasco et al (2010), Gabriel Filho et al (2011, Pereira et al (2008), Putti et al (2014Putti et al ( , 2017aPutti et al ( , 2017b, Viais Neto et al (2019a, 2019b, Martínez (2020), Góes (2021) and Matulovic et al (2021). In this way, after the construction of the fuzzy sets of output, the highest degrees of relevance of each median of treatments were calculated, thus associating the input variables with the output variables.…”