Fuzzy Modeling of the Effects of Different Irrigation Depth in Radish Crop. Part Ii: Biometric Variables Analysis

Abstract

In order to estimate the response of biometric variables in different irrigation depths in radish crop, as well as their relations in the development of the crop, a fuzzy mathematical analysis was carried out from irrigation with depths of different percentages of the crop evapotranspiration (ETc), using Gaussian pertinence functions for the input variable and triangular for the biometric output variables. Validations were performed using neural network models, smoothing splines and polynomial regression. The relation among the biometric variables was measured applying the Pearson correlation coefficient. The results showed that the fuzzy modeling presented superiority in the crop development estimate over the quadratic polynomial regression model, neural network and smoothing splines, because it achieved an average reduction of errors among the biometric variables, of 7.8% 94.6% and 9.2% for the RMSE in the respective models, as well as a better adjustment of the data with average R 2 of the variables. The modeling with neural network showed inadequate agronomic behavior in data representation. Regarding biometric variables, the length and diameter of the tuberous root are inversely correlated, and the fresh phytomass of the tuberous root is correlated only with the fresh phytomass of the root.

Fuzzy modeling of biometric variables development of tomato crop under irrigation and water salinity effects

“…Twelve (3 × 4) combinations between fuzzy sets of the two input variables were considered to obtain the rules base of the fuzzy system in both models. Thus, 12 pairs of the Irrigation × DAS form were created for Model 1 and 12 pairs of the Salinity × DAS form for Model 2, according to the methodology used by Viais Neto et al (2019a), and similar to those adopted by Cremasco, Gabriel Filho, and Cataneo (2010), Gabriel Filho, Cremasco, Putti, and Chacur (2011), Gabriel Filho, Pigatto, and Lourenzani (2015), Gabriel Filho et al (2016), Gabriel Filho, Silva Junior, Cremasco, Souza, and Putti (2022a), Gabriel Filho, Silva, Putti, and Cremasco (2022b), Pereira, Bighi, Gabriel Filho, and Cremasco (2008), Putti, Gabriel Filho, Silva, Ludwig, and Cremasco (2014), Putti et al (2017a), Putti, Kummer, Grassi Filho, Gabriel Filho, and Cremasco (2017b), Putti et al (2021), Putti, Cremasco, Silva Junior, and Gabriel Filho (2022), Martínez et al (2020), Matulovic, Putti, Cremasco, and Gabriel Filho (2021), Góes, Goes, Cremasco, and Gabriel Filho (2022), Boso, Cremasco, Putti, and Gabriel Filho (2021a), Boso, Cremasco, Putti, and Gabriel Filho (2021b), and Maziero, Chacur, .…”

Artificial Intelligence Techniques Applied to the Optimization of Micro-Irrigation Systems by the Zimmermann-Werner Method

“…Several fuzzy systems have been successfully applied in agricultural engineering, such as poultry production and industry companies (Pereira et al, 2019;Cremasco et al, 2010), cattle production (Gabriel Filho et al, 2011Filho et al, , 2016Maziero et al, 2022), irrigation engineering (Viais Neto et al, 2019a, 2019bPutti et al, 2017aPutti et al, , 2021Putti et al, , 2022Boso et al, 2021aBoso et al, , 2021bMatulovic et al, 2021;Gabriel Filho et al, 2022a, 2022b, optimization of agricultural implements (Góes et al, 2021), increased plant vitality (Putti et al, 2014(Putti et al, , 2017b, and market of agricultural products (Gabriel Filho et al, 2015;Martínez et al, 2020).…”

FUZZY MODELING OF SALINITY EFFECTS ON PUMPKIN (Cucurbita pepo) DEVELOPMENT

“…, ViaisNeto et al (2019aNeto et al ( , 2019b,Martínez et al (2020),Matulovic et al (2021),Góes et al (2021),Boso et al (2021aBoso et al ( , 2021b, andMaziero et al (2022).…”