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

Matulovic, Mariana; Putti, Fernando Ferrari; Cremasco, Camila Pires; Gabriel, Luís R. A.

doi:10.4025/actasciagron.v43i1.51384

Cited by 14 publications

(9 citation statements)

References 22 publications

(18 reference statements)

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“…Following the rules, Mamdani inference method was employed to calculate the rate of the output variables. This methodology was used similarly by Cremasco et al (2010), Gabriel Filho et al (2011, Pereira et al (2008), Putti et al (2014Putti et al ( , 2017aPutti et al ( , 2017bPutti et al ( , 2021Putti et al ( , 2022 Analysis of variance and mean comparisons were carried out by Tukey's test at 5% probability using the Minitab 16 statistical software.…”

Section: Fuzzy Modelmentioning

confidence: 99%

Fuzzy Modeling of Salinity Effects on Radish Yield Under Reuse Water Irrigation

et al. 2022

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The increase of water usage for food production in recent years has triggered researches on ways to optimize the use of water and to reuse saline water. The present study analyzes the effects of reused saline water in the irrigation of radish culture and the construction of a fuzzy logical mathematic model so that producers may evaluate their production. The experiment, measuring the development of radish bulb at five saline water levels, was developed in a greenhouse in the Botucatu, Campus of the São Paulo State University (UNESP), Botucatu/SP, Brazil. Results showed that salinity caused reduction in fresh and dry matter of the bulb and affected ratings throughout the culture cycle. From the developed fuzzy model, it was possible to verify that the fuzzy modeling helps in the analysis of the experimental data and makes it possible to perform simulations capable of inferring points that were not experimentally determined.

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Section: Fuzzy Modelmentioning

confidence: 99%

Fuzzy Modeling of Salinity Effects on Radish Yield Under Reuse Water Irrigation

et al. 2022

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“…Thus, the fuzzy sets 𝐶 of the input variable, 1 ≤ 𝑖 ≤ 6, were associated with the fuzzy sets 𝐶 of the output variables, 1 ≤ 𝑗 ≤ 𝑛, with these last sets being created from the means of experimental treatments. This methodology was used similarly by Cremasco et al (2010), Gabriel Filho et al (2011, Pereira et al (2008), Putti et al (2014Putti et al ( , 2017aPutti et al ( , 2017bPutti et al ( , 2021 The inference method used to calculate the numerical value of the output variables, according to the rules base, was from Mamdani (Mamdani & Assilian, 1975).…”

Section: Experiments Descriptionmentioning

confidence: 99%

Fuzzy Modeling of the Effect of Irrigation Depths on Beet Cultivars

et al. 2022

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The objective of this study was to develop a Fuzzy Rule-Based System (FRBS) for the mathematically modeling of the irrigation level effect on beet cultivars (Beta vulgaris L.). From an agronomic experiment carried out in a protected environment (greenhouse), it was defined as input variables, for each cultivar, the irrigation levels (depths of 25, 40, 55, 70, 85, and 100% of ETc), in which ETc is the crop evapotranspiration [mm d -1 ], while the output variables were productivity, root diameter, and plant height. Was adopted the inference method of Mamdani and centroid method for fuzzification. The fuzzy models performed better than polynomial regression models, when compared with the coefficients of determination (R²), mean absolute error (MAE) and root mean square error (RMSE). In addition, the excellent performance of the model is highlighted due to the curve containing all the points of the treatments means. As agronomic results, concluded that the Early Wonder cultivar has higher yields when submitted to water deficit.

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“…To obtain the basis of rules for the fuzzy system, the 15 (5 × 3) combinations between the fuzzy sets of the input variables were considered, thus creating 15 pairs of the form (DAT × Salinity). This methodology was used similarly by Cremasco et al (2010), Gabriel Filho et al (2011, 2015, 2016, Pereira et al (2008), Putti et al (2014Putti et al ( , 2017aPutti et al ( , 2017bPutti et al ( , 2021Putti et al ( , 2022 A value of the output variable was associated with each day after transplantation (DAT) and salinity level (S). That value, in turn, was related to the fuzzy set at the highest degree of membership.…”

Section: Fuzzy Model Developmentmentioning

confidence: 99%

“…, 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).…”

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confidence: 99%

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

et al. 2022

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The low quality of the water currently used for irrigation purposes harms the quality of the products and can lead to a reduction in production. Thus, the objective of this work was to verify the effect of saline water in irrigation water, on biometric variables of pumpkin crop using a system based on fuzzy rules. The agronomic experimental part of this work was carried out in a greenhouse. The experimental design was in randomized blocks, with 5 doses of salinity (0, 1.25, 2.5, 3.75, and 5 dS m -1 ) and with 5 repetitions. The salinity doses and evaluations carried out throughout the cycle (days after transplanting) were defined as input variables in the mathematical model. For the output variables, the collected biometric responses were defined: number of leaves, number of flowers, leaf fresh mass, leaf dry mass, stem fresh mass, stem length, root fresh mass, and root length. After evaluation, the mathematical model was developed and its validation was carried out using statistical methods and Receiver Operating Characteristic Curves (ROC). It was observed that salinity affects the bush pumpkin crop with a reduction in the evaluated parameters. The mathematical model proved to be efficient for this evaluation.

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

Cited by 14 publications

References 22 publications

Fuzzy Modeling of Salinity Effects on Radish Yield Under Reuse Water Irrigation

Fuzzy Modeling of Salinity Effects on Radish Yield Under Reuse Water Irrigation

Fuzzy Modeling of the Effect of Irrigation Depths on Beet Cultivars

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

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