Abstract
Abstract The aim of this study was to compare the Multiple Linear Regression and Artificial Neural Network models in prediction of grain yield of ten landrace varieties of lima bean and evaluate adaptability and stability through the Lin and Binns method for identification of the best performing variety. Trials were conducted in the municipalities of Teresina, PI, and Sao Domingos do Maranhao, MA, through measurement of 12 traits, except for grain yield in Sao Domingos do Maranhao. The parameters of Pearson and Spearman correlation, root mean square error, mean absolute error, and coefficient of determination were used to compare the models. The Artificial Neural Network proved to be more adequate for prediction of grain yield. Adaptability and stability analyses indicated that the environments are discriminant for selection of promising genotypes, and that the landrace variety Mulatinha can be recommended for planting in the municipalities.
Highlights
The development and application of modeling in agriculture is an important tool that can serve to guide research, technological management, and decisionmaking (Corrêa et al 2011)
The following traits were measured in both experiments, according to the descriptors for Phaseolus lunatus L. (IPGRI 2001): number of days to flowering (NDF), number of days
All the analyses described in this study were performed with the assistance of the keras package and functions implemented in the R software (R Core Team 2018)
Summary
The development and application of modeling in agriculture is an important tool that can serve to guide research, technological management, and decisionmaking (Corrêa et al 2011). In this context, the use of mathematical models such as multiple linear regression and artificial neural networks allows correlation of agronomic traits with genotype performance in the field. Regression analysis models and investigates the relationship among variables, studying the dependence of the trait of interest in relation to one or more independent variables (Gujarati 2000) For their part, artificial neural networks are computational techniques inspired by the neural architecture of the human brain, which acquires knowledge through experience (Braga et al 2012). Torkashvand et al (2017) used Multilayer perceptrons (MLPs) to predict fruit firmness in kiwi varieties
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