FUZZY MODELING OF THE EFFECTS OF DIFFERENT IRRIGATION DEPTH IN RADISH CROP. PART II: BIOMETRIC VARIABLES ANALYSIS
| Autor: | Ana Cláudia Marassá Roza Boso, Fernando Ferrari Putti, Camila Pires Cremasco, Luís Roberto Almeida Gabriel Filho |
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| Přispěvatelé: | Universidade Estadual Paulista (Unesp) |
| Rok vydání: | 2021 |
| Předmět: |
Polynomial regression
Biometrics Mean squared error neural network Agriculture (General) Regression analysis biometric variables 04 agricultural and veterinary sciences Quadratic function irrigation depth Agricultural and Biological Sciences (miscellaneous) Fuzzy logic Pearson product-moment correlation coefficient S1-972 Smoothing spline symbols.namesake polynomial regression Computer Science::Computer Vision and Pattern Recognition Statistics 040103 agronomy & agriculture symbols 0401 agriculture forestry and fisheries fuzzy logic Mathematics |
| Zdroj: | SciELO Repositório Institucional da UNESP Universidade Estadual Paulista (UNESP) instacron:UNESP Engenharia Agrícola, Volume: 41, Issue: 3, Pages: 319-329, Published: 25 JUN 2021 Engenharia Agrícola, Vol 41, Iss 3, Pp 319-329 (2021) Engenharia Agrícola v.41 n.3 2021 Engenharia Agrícola Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
| ISSN: | 1809-4430 0100-6916 |
| Popis: | Made available in DSpace on 2021-07-14T10:21:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-06-25. Added 1 bitstream(s) on 2021-07-14T11:27:56Z : No. of bitstreams: 1 S0100-69162021000300319.pdf: 915553 bytes, checksum: 032aa323077110649b223df7fe9ee77a (MD5) 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 R2 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. São Paulo State University, School of Agriculture São Paulo State University, School of Sciences and Engineering São Paulo State University, School of Agriculture São Paulo State University, School of Sciences and Engineering |
| Databáze: | OpenAIRE |
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