Fuzzy modeling of vegetable straw cover crop productivity at different nitrogen doses

Góes, Bruno César; Góes, Renato Jaqueto; Cremasco, Camila Pires; Filho, Luís Roberto Almeida Gabriel

doi:10.1007/s40808-021-01125-4

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…The construction and development of the fuzzy modeling, was initially analytical and subsequently structuring it mathematically with the aid of electronic spreadsheets (Excel software) and by the Fuzzy Logic Toolbox, present in Matlab® software. Similar systems can be found in Cremasco et al (2010), Cremasco et al (2015) Gabriel Filho et al (2011Filho et al ( , 2016, Pereira et al (2008), Putti et al (2014Putti et al ( , 2017aPutti et al ( , 2017b, Viais Neto et al (2019a, 2019b, Martínez et al (2020) and Góes et al (2021).…”

Section: Fuzzy Modelingsupporting

confidence: 62%

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

et al. 2021

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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.

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Section: Fuzzy Modelingsupporting

confidence: 62%

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

et al. 2021

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“…For cases that require this smoothness, the use of the Gaussian membership function is more appropriate. This procedure was used similarly by Cremasco et al (2010), Gabriel Filho et al (2011, 2015, Pereira et al (2008), Putti et al (2014Putti et al ( , 2017aPutti et al ( , 2017b, Viais Neto et al (2019a, 2019b, Martínez et al (2020), Matulovic et al (2021) and Góes et al (2021).…”

Section: Fuzzy Systemmentioning

confidence: 99%

Fuzzy Modeling of the Effects of Different Irrigation Depths on the Radish Crop. Part I: Productivity Analysis

et al. 2021

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The productivity of a crop is related to the water demand inserted in its development. The measurement of water and its optimization directly influences the final costs of crop production for agricultural producers. In this sense, the objective of this study is evaluating the fuzzy modeling in estimating the productivity of the radish crop (fresh phytomass of the tuberous root) affected by different irrigation depths (25%, 50%, 75%, 100%, and 125%), based on evapotranspiration of the crop (ETc). To measure the results, two fuzzy systems (with triangular and Gaussian membership functions respectively) and a polynomial regression model were developed to perform model validation comparisons. The fuzzy modeling showed a better fit of the data compared to the polynomial regression model, with reduced errors (RMSE with values 6.3 and 6.9 in the fuzzy models versus 8.8 in the regression model) and higher correlation coefficient (0.54 and 0.5 fuzzy versus 0.1 regression). The triangular fuzzy model estimated the best crop yield (31.9 g of fresh phytomass) when using a 100% ETc depth. Also, the curve generated by the fuzzy model accurately represents all the productivity averages in each depth, in addition to this model presenting the smallest errors (compared to the triangular model and the regression model) and the highest R². However, the Gaussian fuzzy model proved to be more efficient in representing the agronomic reality, as it does not have peaks and valleys, and it is a smooth model in both growth and degrowth.

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“…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.…”

Section: Rule Basementioning

confidence: 99%

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

et al. 2021

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Fuzzy modeling of vegetable straw cover crop productivity at different nitrogen doses

Cited by 12 publications

References 21 publications

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

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

Fuzzy Modeling of the Effects of Different Irrigation Depths on the Radish Crop. Part I: Productivity Analysis

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

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