Method of using the Fuzzy Logic Toolbox of MATLAB software for mathematical modeling of biometric and nutritional variables of soybean culture
DOI:
https://doi.org/10.33448/rsd-v9i10.8938Keywords:
Artificial intelligence; Fuzzy systems; Fuzzy logic.Abstract
Fuzzy logic was introduced into the scientific world in the 1960s by the then mathematician Lotif Asker Zadeh. Its concept is based on the non-probabilistic uncertainty principle approach, composed of subjectivity and imprecision in the linguistic terms of the information, assigning values for the degree of relevance between 0 and 1. Fuzzy logic is present in the most diverse fields of activity, from aircraft construction to widespread use in the medical field. Thus, its use has been intensifying in the field of agrarian sciences, as it has a greater degree of accuracy in relation to statistical models, carried out by agronomic experiments. The objective was to carry out a didactic description of the fuzzy methodology used to build a fuzzy system applied to soybean cultivated under no-tillage system. For modeling, the MATLAB R2019a software was used, in which the screen was printed for each step during the construction of the model, in order to contribute to the wide dissemination of fuzzy systems in agricultural sciences.
References
Anjos, J. C. A (2001). Um sistema de avaliação de produtividade em assentamentos rurais utilizando lógica fuzzy. 82 f. Dissertação (Mestrado) - Pós-Graduação em Ciência da Computação, Universidade Federal de Santa Catarina.
Cremasco, C. P.; Gabriel Filho, L. R. A., & Cataneo, A. (2010) Methodology for determination of fuzzy controller pertinence functions for the energy evaluation of poultry industry companies. Energia na Agricultura, 259(3):21-39.
Gabriel Filho, L. R. A.; Cremasco, C. P.; Putti, F. F., & Chacur, M. G. M. (2011) Application of fuzzy logic for the evaluation of livestock slaughtering. Engenharia Agrícola, 31(4):813-825.
Gabriel Filho, L. R. A.; Pigatto, G. A. S., & Lourenzani, A. E. B. S. (2015) Fuzzy rule-based system for evaluation of uncertainty in cassava chain. Engenharia Agrícola, 35(2):350-367.
Gabriel Filho, L. R. A.; Putti, F. F.; Cremasco, C. P.; Bordin, D.; Chacur, M. G. M., & Gabriel L. R. A. (2016) Software to assess beef cattle body mass through the fuzzy body mass index. Engenharia Agrícola, 36(1): 179-193.
Goes, R. J. (2016). Doses de Nitrogênio em Coberturas Vegetais e Molibdênio Foliar na Soja em Sucessão. 77 f. Tese (Doutorado) - Pós-Graduação em Agronomia (Sistemas de Produção), Universidade Estadual Paulista.
Janarthanan, R.; Balamurali, R.; Annapoorani, A., & Vimala, V. (2020). Prediction of rainfall using fuzzy logic. Materials Today: Proceedings, S2214785320346332.
Li, M.; Sui, R.; Meng, Y., & Yan, H. (2019). A real-time fuzzy decision support system for alfalfa irrigation. Computers and Electronics in Agriculture, 163, 104870.
Mamdani, E. H., & Assilian, S. (1975). An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller. International Journal of Man-Machine Studies, 7(1), 1–13.
Martínez, M. P.; Cremasco, C. P.; Gabriel Filho, L. R. A.; Braga Junior, S. S.; Bednaski, A. V.; Quevedo-Silva, F.; Correa, C. M.; Silva, D., & Padgett, R. C. M. L. (2020) Fuzzy inference system to study the behavior of the green consumer facing the perception of greenwashing. Journal of Cleaner Production, 242: 116064.
Pereira, D. F.; Bighi, C. A.; Gabriel Filho, L. R. A., & Cremasco, C. P. C. (2008) Sistema fuzzy para estimativa do bem-estar de matrizes pesadas. Engenharia Agrícola, 28(4):624-633.
Phuong, N. H., & Kreinovich, V. (2001). Fuzzy logic and its applications in medicine. International Journal of Medical Informatics, 62(2–3), 165–173.
Prabakaran, G.; Vaithiyanathan, D., & Ganesan, M. (2018). Fuzzy decision support system for improving the crop productivity and efficient use of fertilizers. Computers and Electronics in Agriculture, 150, 88–97.
Putti, F. F.; Gabriel Filho, L. R. A.; Cremasco, C. P.; Bonini Neto, A.; Bonini, C. S. B., & Reis, A. R. (2017a) A Fuzzy mathematical model to estimate the effects of global warming on the vitality of Laelia purpurata orchids. Mathematical Biosciences, 288:124-129.
Putti, F. F.; Gabriel Filho, L. R. A.; Silva, A. O.; Ludwig, R., & Cremasco, C. P. (2014) Fuzzy logic to evaluate vitality of catasetum fimbiratum species (Orchidacea). Irriga, 19(3):405-413.
Putti, F. F.; Kummer, A. C. B.; Grassi Filho, H.; Gabriel Filho, L. R. A., & Cremasco, C. P. (2017b) Fuzzy modeling on wheat productivity under different doses of sludge and sewage effluent. Engenharia Agrícola, 37(6):1103-1115.
Putti, F. F. (2015). Análise dos indicadores biométricos e nutricionais da cultura da alface (Lactuca sativa L.) irrigada com água tratada magneticamente utilizando modelagem fuzzy. 186 f. Tese (Doutorado) - Pós-Graduação em Agronomia (Irrigação e Drenagem), Universidade Estadual Paulista.
Ren, Z.; Liao, H., & Liu, Y. (2020). Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the COVID-19. Computers & Industrial Engineering, 145, 106517.
Rignel, D. G.; Chenci, G. P., & Lucas, C. A. (2011). Uma introdução a lógica fuzzy. Revista Eletrônica de Sistemas de Informação e Gestão Tecnológica, 1(1), 12.
Salgado, C. M.; Vieira, S. M.; Mendonça, L. F.; Finkelstein, S., & Sousa, J. M. C. (2016). Ensemble fuzzy models in personalized medicine: Application to vasopressors administration. Engineering Applications of Artificial Intelligence, 49, 141–148.
Slavyanov, K. (2018). An algorithm of fuzzy inference system for human resources selection tools. Society Integration Education. Proceedings of the International Scientific Conference, 5, 445–454.
Viais Neto, D. S.; Cremasco, C. P.; Bordin, D.; Putti, F. F.; Silva Junior, J. F., & Gabriel Filho, L. R. A. (2019a) Fuzzy modeling of the effects of irrigation and water salinity in harvest point of tomato crop. Part I: description of the method. Engenharia Agrícola, 39(3):294-304.
Viais Neto, D. S.; Cremasco, C. P.; Bordin, D.; Putti, F. F.; Silva Junior, J. F., & Gabriel Filho, L. R. A. (2019b) Fuzzy modeling of the effects of irrigation and water salinity in harvest point of tomato crop. Part II: application and interpretation. Engenharia Agrícola, 39(3):305-14.
Viais Neto, D. S. (2016). Modelagem fuzzy para avaliação do desenvolvimento do tomate em tensões de água no solo e doses de salinidade na irrigação. 70 f. Tese (Doutorado) - Pós-Graduação em Agronomia (Irrigação e Drenagem), Universidade Estadual Paulista.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
Zimmermann, H. J. (2010). Fuzzy set theory: Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 317–332.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Bruno César Góes; Renato Jaqueto Goes; Camila Pires Cremasco; Luís Roberto Almeida Gabriel Filho
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
