Fuzzy logic in automation for interpretation of adaptability and stability in plant breeding studies

Abstract

The methods of Annicchiarico (1992) and Cruz et al. (1989) are widely used in phenotypic adaptability and stability analyses in plant breeding. In spite of the importance of these methodologies, their parameters are difficult to interpret. The aim of this research was to develop fuzzy controllers to automate the decision-making process employed by adaptability and stability studies following the methods adopted by Annicchiarico (1992) and Cruz et al. (1989) and check their efficiency using experimental data from common bean cultivars. Fuzzy controllers have been developed based on the Mamdani inference system proposed by these two methods of adaptability and stability studies. For the first fuzzy controller parameters were considered favorable environments and the recommendation index for unfavorable environments obtained by Annicchiarico's method (1992). For the second controller the parameters considered were the general mean (β 0), coefficient of regression of unfavorable environments (β 1) and coefficient of favorable environments (β 1i + β 2i) and the coefficient of determination of the method of Cruz et al. (1989). To check the performance of these drivers yield data from field trials on 18 common bean cultivars grown in 11 environments were used. The controllers were developed from established routines in the R software and, using the inference system based on the methods proposed by Annicchiarico (1992) and Cruz et al. (1989), classified the 18 genotypes appropriately in accordance with the criteria for each method. Thus, the methods used are effective, and are prescribed for decision-making automation in yield adaptability and stability studies pertaining to recommendation of cultivars.

Application of fuzzy logic for adaptability and stability studies in flood‐irrigated rice (Oryza sativa)

“…The efficiency of these controllers is due to their powerful ability to model complex human reasoning (Türksen, 2007). Carneiro et al (2019) suggested that this technique may become an important tool in breeding programmes owing to its simplicity and automation capability, especially when a large number of genotypes are evaluated. The current study also demonstrates the utility of automated decision‐making in assessing the adaptability and stability based on a hybrid model of Eberhart & Russell and Lin & Binns modified by Carneiro (1998) for rice.…”

Multi-information analysis for recommendation of flooded-irrigated rice for adaptability and phenotypic stability

“…Other contributions refer to different statistical modeling capable of concisely capturing these concepts for use by breeders. Thus, as an example, current computational intelligence methodologies (Nascimento et al 2013;Teodoro et al, 2015) or logic fuzzy (Carneiro et al, 2018;Carneiro et al, 2019) are interesting because they allow for machine learning less subjective interpretations of information or concepts already presented decades ago by Eberhart and Russell (1966) or Lin and Binns (1988). Techniques such as GGE biplot and AMMI use the interaction phenomenon (GxE) and allow, through a series of graphical analyzes, interpretations of environments and genotypes simultaneously where invariance, responsiveness, and similarity of response patterns can be visualized.…”

“…In this context, to assess adaptability and stability, various statistical methods are used, and differ in statistical principles, biometric procedures, and interpretation of results (Eeuwijk et al 2016). According to Cargnelutti Filho, Perecin, Malheiros and Guadagnin (2007), these methods can be arranged in several classes, such as those based on analysis of variance (Yates & Cochran, 1938;Plaisted & Peterson, 1959;Wricke, 1965), linear regression (Finlay & Wilkinson, 1963;Eberhart & Russell, 1966;Tai, 1971), bi-segmented regression (Verma, Chahal, & Murty, 1978;Cruz, Torres, & Vencovsky, 1989) in non-parametric statistics (Lin & Binns, 1988;Huehn, 1990;Annicchiarico, 1992;Rocha, Muro-Abad, Araujo, & Cruz, 2005;Nascimento et al, 2010;Nascimento et al, 2015), quantile regression (Barroso et al, 2015), bayesian statistics (Couto et al, 2015;Nascimento et al, 2011) and computational intelligence (Nascimento et al, 2013;Teodoro et al, 2015;Carneiro et al, 2018;Carneiro et al, 2019).…”

Using fuzzy logic to select coloured-fibre cotton genotypes based on adaptability and yield stability

“…To circumvent this limitation, fuzzy logic mathematical modelling can be used to simulate the approximations and uncertainties of human logic (Carneiro et al, 2017;Carneiro et al, 2019) and assign degrees of pertinence to the elements. Fuzzy logic has been used to select common bean cultivars (Carneiro et al, 2017) and to select common bean cultivars for adaptability and stability (Carneiro et al, 2017).…”

“…Linguistic fuzzy logic used with the Sugeno fuzzy controllers for the adaptability and stability of genotypes, based on theEberhart and Russell (1966) method and adapted fromCarneiro et al (2019).…”