Machine Learning for Genetic Data Analysis and Precision Crop Breeding

Dear Colleagues,

The rapid development of machine learning (ML) has opened new perspectives in the analysis of genetic data and its application to crop breeding. Recent advances in high-throughput genotyping, phenotyping, and environmental data collection have generated vast datasets that require intelligent computational methods to unlock their full potential. Machine learning offers powerful tools to revolutionize the way researchers handle large-scale genomic, phenotypic, and environmental datasets, enabling the identification of complex trait–gene associations, the prediction of crop performance under variable conditions, and the design of improved breeding strategies

This Special Issue aims to collect cutting-edge research and comprehensive reviews that explore novel machine learning methodologies, genomic prediction models, multi-omics integration, image-based phenotyping, and other ML-driven approaches for genetic data analysis and precision breeding. We warmly invite researchers and scholars to contribute by submitting their original research articles or reviews in this dynamic and interdisciplinary field.

Dr. Maldonado Carlos
Dr. Freddy Mora-Poblete
Guest Editors

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