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

Putti, Fernando Ferrari; Lanza, Márcio Henrique; Filho, Hélio Grassi; Cremasco, Camila Pires; Souza, Angela Vacaro de; Filho, Luís Roberto Almeida Gabriel

doi:10.1590/1809-4430-eng.agric.v41n2p204-214/2021

Cited by 16 publications

(8 citation statements)

References 44 publications

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“…As a result, 17 pairs of the form (TCST × Area) were generated. This approach aligns with the method utilized by Mamann et al (2020) e Putti et al (2021).…”

Section: Fuzzy System For Estimating Productivity Of Sugarcane Suppliersmentioning

confidence: 79%

“…These systems have been widely employed in Agricultural Sciences, including poultry companies and production (CREMASCO; GABRIEL FILHO; CATANEO, 2010;PEREIRA et al, 2008), cattle farming (GABRIEL FILHO et al, 2011, 2016MAZIERO et al, 2022), irrigation engineering (BOSO et al, 2021a, b;SAAD;GABRIEL FILHO, 2022;GABRIEL FILHO et al, 2022a, b;MATULOVIC et al, 2021;PUTTI et al, 2017PUTTI et al, , 2021VIAIS NETO et al, 2019a, b), optimization of agricultural implements (GÓES et al, 2022), enhancement of plant vitality (PUTTI et al, 2014(PUTTI et al, , 2017, the agricultural product market (GABRIEL FILHO;PIGATTO;LOURENZANI, 2015;MARTÍNEZ et al, 2020;OLIVEIRA et al, 2021), among others. Specifi cally in the sugar-alcohol sector, applications of fuzzy systems in the classifi cation of sugarcane productivity (NATARAJAN; SUBRAMANIAN; PAPAGEORGIOU, 2016), combustion control in bagasse boilers (MELLO; CRUZ; SOUSA JÚNIOR, 2019), and weed detection in sugarcane fi elds (SUJARITHA et al, 2017) stand out.…”

Section: Introductionmentioning

confidence: 99%

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Estimativa da produtividade de cana-de-açúcar: Uma abordagem utilizando lógica fuzzy

Gabriel Filho,

Amorim,

Cremasco

et al. 2024

RCA

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Brazil is a benchmark in sugarcane production, with the state of São Paulo standing out as the largest Brazilian producer. However, for sugarcane suppliers and mills to sustain this activity, there is a need to improve productivity per hectare and reduce production costs. In this regard, this study aimed to propose fuzzy systems to estimate sugarcane productivity based on planted area (Area) and total cost of soil tillage (TCST) for raw material suppliers and mills. To this end, two fuzzy inference systems were constructed for the output variable (productivity) from two input variables (Area and TCST), considering fi ve membership functions (very low, low, medium, high, and very high). Additionally, a survey on 42 sugarcane suppliers and 31 mills in the state of São Paulo was used for model construction. The results showed that the relationship between Area and TCST refl ects on the productivity of sugarcane suppliers and mills in distinct ways. For suppliers, an increase in productivity is observed when there is an almost negative relationship between both input variables. For mills, productivity rises when these variables fl uctuate in the same direction. Therefore, the proposed method is viable and provides relevant information for conjecturing survival strategies for agents in the sugarcane energy sector.

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“…As a result, 17 pairs of the form (TCST × Area) were generated. This approach aligns with the method utilized by Mamann et al (2020) e Putti et al (2021).…”

Section: Fuzzy System For Estimating Productivity Of Sugarcane Suppliersmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Estimativa da produtividade de cana-de-açúcar: Uma abordagem utilizando lógica fuzzy

Gabriel Filho,

Amorim,

Cremasco

et al. 2024

RCA

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“…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, .…”

Section: Rules Basementioning

confidence: 99%

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

Gabriel Filho,

Viais Neto,

Putti

et al. 2023

Acta Sci. Agron.

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Tomato is a demanding crop in terms of handling, mainly because irrigation has a strong influence on fruit production and quality. Salinity changes the absorption, transport, assimilation, and distribution of nutrients in the plant. In general, such effects are analyzed using statistical tests. However, fuzzy models allow simulations between points that are not verified in agricultural experimentation. Currently, systems with artificial intelligence have excelled in the field of applied sciences, particularly fuzzy systems applied to mathematical modeling. The objective of this research was to use fuzzy modeling to analyze the biometric variables during the development of hybrid tomatoes under two different conditions: the first concerning different water tensions in the soil and the second concerning different salinity doses in irrigation. To this end, two models were developed based on an experiment carried out at São Paulo State University (UNESP), School of Agriculture, Botucatu, São Paulo State, Brazil. Both models sought to estimate the values of biometric variables of the tomato crop. Thus, two models were developed: Model 1 regarded water tensions and days after sowing (DAS), while Model 2 featured salinity and DAS. Fuzzy models provided results that verified the effects of irrigation and salinity layers. Two Fuzzy Rule-Based Systems (FRBS), an input processor with two variables, a set of linguistic rules defined from statistical procedures with percentiles, the Mamdani fuzzy inference method, and the center of gravity method to defuzzification were elaborated for this purpose. The range between −25 and −10 kPa (for Model 1) and between 0.08 and 3 dS m−1 (for Model 2) provided the development within the ideal parameters for the complete development of the plant cycle. The use of fuzzy logic has shown effectiveness in evaluating the development of tomato crops, thus showing potential for use in agricultural sciences. Moreover, the created fuzzy models showed the same characteristics of the experiment, allowing their use as an automatic technique to estimate ideal parameters for the complete development of the plant cycle. The development of applications (software) that provide the results generated by the artificial intelligence models of the present study is the aim of future research.

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“…Na engenharia Agrícola, técnicas de modelagem matemática com inteligência artificial vêm sendo aplicadas com sucesso, na produção e empresas avícolas (Pereira et al, 2008;Cremasco et al, 2010), na produção bovina (Gabriel Filho et al, 2011Maziero et al, 2022), em engenharia de irrigação (Viais Neto et al, 2019a,b;Putti et al, 2017bPutti et al, , 2021Putti et al, , 2022Boso et al 2021a,b;Matulovic et al, 2021;Gabriel Filho et al, 2022a,b), na otimização de implementos Agrícolas (Góes et al, 2022), no aumento da vitalidade das plantas (Putti et al, 2014(Putti et al, , 2017b, no mercado de produtos Agrícolas (Gabriel Filho et al, 2015;Martínez et al, 2020) e na otimização de sistemas de irrigação (Castro et al, 2022).…”

Section: Introductionunclassified

Modelos De Machine Learning Aplicados Na Estimação Da Evapotranspiração De Referência Do Planalto Ocidental Paulista

Souza

Cremasco

Calça

et al. 2022

NAT

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A evapotranspiração depende da interação entre variáveis meteorológicas (radiação solar, temperatura do ar, precipitação, umidade relativa do ar e velocidade do vento) e condições fitossanitárias das culturas agrícolas. É complexo construir medidas confiáveis de evapotranspiração devido aos elevados custos para implantação de técnicas micrometeorológicas, além de dificuldades na operação e manutenção dos equipamentos necessários. O propósito desta pesquisa foi modelar a evapotranspiração de referência (ETo) por meio de técnicas de machine learning em dados climáticos de 30 estações meteorológicas automáticas do Planalto Ocidental Paulista, Estado de São Paulo, Brasil, no período de 2013-2017. Uma comparação do desempenho estatístico entre as técnicas utilizadas foi realizada onde constatou-se melhor desempenho do modelo EToMLP4 (rRMSE = 0.62%), seguido por EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) e EToGRNN4 (rRMSE = 11.05%). Medidas de performance da base de validação evidenciam que os modelos propostos são aptos à estimativa da evapotranspiração de referência com destaque para a técnica MPL. Palavras-chave: evapotranspiração; modelagem matemática; aprendizagem de máquina. Machine learning models applied in the estimation of reference evapotranspiration from the Western Plateau of Paulista ABSTRACT: Evapotranspiration depends on the interaction between meteorological variables (solar radiation, air temperature, precipitation, relative humidity and wind speed) and phytosanitary conditions of agricultural crops. It is complex to build reliable evapotranspiration measurements due to the high costs of implementing micrometeorological techniques, in addition to difficulties in the operation and maintenance of the necessary equipment. The purpose of this research was to model the reference evapotranspiration through machine learning techniques in climatic data from 30 automatic weather stations in the Planalto Ocidental Paulista, State of São Paulo, Brazil, in the period 2013-2017. A comparison of the statistical performance between the techniques used was carried out, where the best performance of the EToMLP4 model (rRMSE = 0.62%), followed by EToANFIS4 (rRMSE = 0.75%), EToSVM4 (rRMSE = 1.19%) and EToGRNN4 (rRMSE = 11.05 %). Performance measures of the validation base show that the proposed models are able to estimate the reference evapotranspiration, with emphasis on the MPL technique. Keywords: evapotranspiration; modeling; machine learning.

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

Cited by 16 publications

References 44 publications

Estimativa da produtividade de cana-de-açúcar: Uma abordagem utilizando lógica fuzzy

Estimativa da produtividade de cana-de-açúcar: Uma abordagem utilizando lógica fuzzy

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

Modelos De Machine Learning Aplicados Na Estimação Da Evapotranspiração De Referência Do Planalto Ocidental Paulista

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