Fuzzy Modeling of the Effects of Irrigation and Water Salinity in Harvest Point of Tomato Crop. Part Ii: Application and Interpretation

Abstract

Tomato, the most popular greenery, is characterized by being a demanding crop in water and when in prolonged and severe drought, has limitations in its growth and reduction in productivity. In addition, this vegetable is affected by excess salinity in the water, which causes leaf wilting, apex and leaf edges burn until their death. Such effects generally are studied using statistical analysis, but there are mathematical theories that allow finer adjustments, highlighting among them, the fuzzy logic. The objective of this study was to evaluate the effects on the growth and yield of hybrid tomato from different water tensions in the soil and different salinity doses in the irrigation at 120 days after sowing using fuzzy modeling. It was used data from an experiment that was conducted in the experimental area at the School of Agriculture of São Paulo State University (UNESP) in Botucatu-SP. The input variables of the fuzzy systems were irrigation and salinity, while the output variables elected were the biometric analysis and productivity that showed statistically significant differences. Analyses of the effects of irrigation and salinity were performed by means of three-dimensional graphics and the output variables contour maps. The variables studied here showed higher values, with exception of plant height for treatment with irrigation in field capacity and water salinity zero.

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.…”

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.…”