Fuzzy Modeling of the Effect of Irrigation Depths on Beet Cultivars

Filho, Luís Roberto Almeida Gabriel; Silva, Alexsandro Oliveira da; Cremasco, Camila Pires; Putti, Fernando Ferrari

doi:10.1590/1809-4430-eng.agric.v42n1e20210084/2022

Cited by 7 publications

(5 citation statements)

References 41 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mathematical models can optimize decision-making and reduce the use of natural resources, being increasingly useful for agricultural activities to work properly in the context of dynamic agriculture (Gabriel Filho et al, 2022), also leading to the possibility of savings in production costs.…”

Section: Introductionmentioning

confidence: 99%

“…Several fuzzy systems have been successfully applied in agricultural engineering, such as cattle production (Maziero et al, 2022), irrigation (Viais Neto et al, 2019Putti et al, 2017Putti et al, , 2021Matulovic et al, 2021;Gabriel Filho et al, 2022), optimization of agricultural implements (Góes et al, 2022), increase in plant vitality (Putti et al, 2017), and market of agricultural products (Martínez et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Modeling as Support for Decision-Making in the Production of Irrigated Coriander Under Mulch in the Semi-Arid Region

et al. 2023

View full text Add to dashboard Cite

Reducing water consumption by crops in semi-arid regions is an important factor for the sustainability of agriculture in these locations. In this sense, this study aims to evaluate the neuro-fuzzy inference method as a support for decision-making in irrigated coriander cultivation. The experiment was performed in two cultivation cycles in Pentecoste-CE, Brazil. The experiment was conducted in randomized blocks arranged in a split-plot design with five primary treatments, consisting of irrigation depths (50, 75, 100, 125, and 150% of the localized evapotranspiration, ETcloc), and five secondary treatments, consisting of different levels of bagana mulch (0, 25, 50, 75, and 100%, equivalent to 16 t ha −1 ). Neuro-fuzzy models with two input variables and eight output biometric variables were developed to evaluate growth (plant height, number of roots, and root length) and yield variables (productivity and shoot and root fresh and dry mass). In the first cycle, the best results occurred close to 55% ETcloc and between 40 and 50% of mulch; in the second cycle, water consumption returned results between 50 and 80% ETcloc. The fuzzy and multiple regression models showed MAE, MSE, and RMSE errors of 9, 22, and 10% lower, respectively. The neuro-fuzzy model might be a viable option for decision-making in irrigated crops, being able to optimize the use of natural resources and available water in semi-arid regions. The use of 55% of irrigation depth and a range of 40 to 50% of mulch can be a strategy for a higher water use efficiency.

show abstract

“…For such situations that involve decision making in complex and uncertain environments, including the agri-food field, the fuzzy set theory is a promising alternative for solving these problems (Tomasiello & Alijani, 2021). In the agricultural field, fuzzy models have been widely used in several areas, such as to evaluate the effects of different irrigation depths (Gabriel Filho et al, 2022) and salinity (Putti et al, 2022) on the yield of irrigated crops, development of models for the eye temperature of cattle under heat stress conditions (Lins et al, 2021 b) and for the eyeball and crest temperatures of laying hens .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Modeling for the Analysis of Different Light Intensities in the Production of Bell Pepper Seedlings

et al. 2022

View full text Add to dashboard Cite

Bell peppers are one of the most cultivated and consumed vegetables in Brazil, and their high production yield largely depends on the use of high-quality seedlings. Supplementary lighting using light-emitting diodes (LEDs) can help in the production of seedlings and improve their quality. This study aims to develop a fuzzy model to predict the agronomic parameters of bell pepper seedlings under different light intensities. Experimental data on the lighting levels and biometric responses of bell pepper seedlings were used to develop a fuzzy model, and the lighting levels defined in five sets were used as the input variable. The model was used to predict the output variables, namely the plant height, number of leaves, stem diameter, internode number, shoot fresh weight, and root fresh weight. The results indicated that the use of supplemental lighting at a level of 75 µmol m -2 s -1 was the most beneficial, producing seedlings with more leaves, higher stem diameters, internode numbers, and fresh weights. The model error rates were satisfactory for the predicted values of all variables, demonstrating the usefulness of the fuzzy model for estimating the best lighting levels in the cultivation of bell pepper seedlings.

show abstract

Fuzzy Modeling of the Effect of Irrigation Depths on Beet Cultivars

Cited by 7 publications

References 41 publications

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

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

Neuro-Fuzzy Modeling as Support for Decision-Making in the Production of Irrigated Coriander Under Mulch in the Semi-Arid Region

Fuzzy Modeling for the Analysis of Different Light Intensities in the Production of Bell Pepper Seedlings

Contact Info

Product

Resources

About