Research trends in maize breeding: A global perspective from bibliometric and topic modelling approaches

Abstract

Maize breeding has undergone significant changes over the past two decades, primarily driven by the rapid development of new technologies, particularly artificial intelligence. Alongside genomic selection, which has already been implemented in commercial breeding programs, highly sophisticated tools for prediction, decision-making, data integration, and interpretation now form the foundation of the modern approach to maize breeding. To understand the impact of latest technologies on maize breeding, we conducted a comprehensive literature analysis by applying Bibliometric science mapping and Latent Dirichlet Allocation (LDA). We formulate the following search query to retrieve the publications from the Web of Science Core Collection: ALL: ("artificial intelligence" OR "machine learning" OR "deep learning" OR "neural network" OR "AI" OR "ML "OR "genomic prediction") AND ALL: ("maize" OR "corn") AND ALL: ("breeding" OR "genetic improvement" OR "selection"). After the detailed article selection and data preparation, 480 publications of 1.643 search results were used as the input for the analysis. The overall trend of the advanced maize breeding literature showed that the number of publications increased during the observed time-span (2010-2024) with the annual growth rate of 32.8%. In total, 60 countries are active in the field of advanced maize breeding with the high degree of the international collaboration. The top five countries according to the number of publications were: China, USA, Mexico, Germany and Brazil. Latent Dirichlet Allocation (LDA) analysis revealed fifteen topics, indicating three main domains influencing advanced maize breeding; i) genetics and genomics; ii) computer science and machine learning; and iii) phenotyping and sensing. The analysis provides important information regarding the future direction of maize breeding and highlights anticipated developments associated with the integration of novel technological advancements