Extreme learning machine for genomic prediction of rust disease resistance in Arabica coffee - Tavela da Silva, Jackson; Valiati Barreto, Cynthia Aparecida; Campana Nascimento, Ana Carolina; Ferreira Azevedo, Camila; Pires de Almeida, Dênia; Teixeira Caixeta, Eveline; Formiga Teixeira, Filipe Ribeiro; Nascimento, Moysés (Pesquisa Agropecuaria Brasileira, 2026)
The objective of this work was to investigate the use of Extreme Learning Machines (ELM) for the genomic prediction of rust resistance in Coffea arabica. With the objective of identifying an effective predictive model for the selection of resistant genotypes, ELM was compared to Artificial Neural Networks (ANN) and Bayesian Generalized Linear Regression (GBLR) in terms of accuracy measures and computational time. To this end, an F2 population of 245 C. arabica plants genotyped with 137 markers was used to evaluate the application of ELM for the genomic prediction of coffee rust resistance. The results indicate that ELM and ANN show a higher accuracy – on average 15% greater than that of GBLR – in predicting rust resistance. Additionally, ELM proves to be computationally more efficient, with a processing speed 5.5 and 19.45 times slower than that of ANN and BGLR, respectively, making it promising for large-scale analyses.