Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits.

ABSTRACT: Improving genetic yield potential in major food grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Yield is a complex trait and reliant on various related variables called yield components. In this study, the five most important yield component traits in soybean were measured using a panel of 250 genotypes grown in four environments. These traits were the number of nodes per plant (NP), number of non-reproductive nodes per plant (NRNP), number of reproductive nodes per plant (RNP), number of pods per plant (PP), and the ratio of number of pods to number of nodes per plant (P/N). These data were used for predicting the total soybean seed yield using the Multilayer Perceptron (MLP), Radial Basis Functi

SUBMITTER: Yoosefzadeh-Najafabadi M 

PROVIDER: S-EPMC8087002 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature