Abstract
In the last decades, a new trend to use more refined analytical procedures, such as artificial neural networks (ANN), has emerged to be most accurate, efficient, and extensively applied for mining and data prediction in different contexts, including plant breeding. Thus, this study was developed to establish a new classification proposal for targeting genotypes in breeding programs to approach classical models, such as a complete diallel and modern prediction techniques. The study was based on the standard deviation values of an interpopulation diallel and it also verified the possibility of training a neural network with the standardized genetic parameters for a discrete scale. We used 12 intercrossed maize populations in a complete diallel scheme (66 hybrids), evaluated during the 2005/2006 crop season in three different environments in southern Brazil. The implemented MLP architecture and other associated parameters allowed the development of a generalist model of genotype classification. The MLP neural network model was efficient in predicting parental and interpopulation hybrid classifications from average genetic components from a complete diallel, regardless of the evaluation environment.
Highlights
In plant breeding projects, techniques to reduce the number of crosses or predict the performance of missing hybrids efficiently tend to optimize breeding programs to focus on promising crosses
The study was based on the standard deviation values of an interpopulation diallel and it verified the possibility of training a neural network with the standardized genetic parameters for a discrete scale
The multilayer perceptron neural networks (MLP) are considered a derivation of artificial neural networks (ANN), since they involve more than one layer hidden in the modeling process
Summary
Techniques to reduce the number of crosses or predict the performance of missing hybrids efficiently tend to optimize breeding programs to focus on promising crosses. According to Peixoto et al (2015), a new trend with the use of enhanced analytical procedures, such as artificial neural networks-ANN, has emerged and these techniques are considered more accurate, efficient, and extensively applied for mining and data prediction, according to Rad (2018). The multilayer perceptron neural networks (MLP) are considered a derivation of ANNs, since they involve more than one layer hidden in the modeling process. The use of single or multilayer neural networks has gained relevance in recent years and has shown to be efficient in analyzing complex systems to predict yield (Leal et al, 2015; Soares et al, 2015), determine physiological activities of plants (Feng et al, 2017; Abrishami et al, 2019), and identify diseases through images (Zhang et al, 2018), among other applications
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
