Open AccessMACHINE LEARNING FOR CROP SCIENCE: APPLICATIONS AND PERSPECTIVES IN MAIZE BREEDING
Author(s) -
Ana Carolina Lorena,
ALEXANDRE HILD AONO,
Ricardo José Gonzaga Pimenta,
FELIPE ROBERTO FRANCISCO,
Anete Pereira de Souza
Publication year - 2022
Publication title -
revista brasileira de milho e sorgo
Language(s) - English
Resource type - Journals
eISSN - 1980-6477
pISSN - 1676-689X
DOI - 10.18512/rbms2022vol21e1257
Subject(s) - computer science , context (archaeology) , machine learning , task (project management) , artificial intelligence , data science , throughput , comprehension , biology , engineering , systems engineering , paleontology , telecommunications , wireless , programming language
Machine learning (ML) has been a major driver in complex data analysis in recent decades, allowing the mining of large databases. ML techniques allow the creation of computational models for prediction, pattern extraction and recognition, considering the premise that the computer acquires learning skills to perform a given task without being explicitly programmed for such a purpose. Driven by the efficiency of these techniques, several studies have demonstrated their wide range of applications and high potential for maize breeding. From the prediction of genetic values by omic data to applications of high-throughput phenotyping data, ML models have promoted advances in the species comprehension and assisted in the development of more effective tools for its breeding, driving expressive yield gains. In this context, this work presents the main contributions of ML in maize breeding, providing a broad view of the main studies and methodological perspectives in the area.