Artificial Intelligence and Machine Learning in Plant Breeding: Data-driven Frameworks for Genomic Prediction, High-throughput Phenotyping and Accelerated Cultivar Development

Plant breeding is increasingly a data-intensive science. Modern programmes generate genome-wide marker data, multi-environment trial records, remote-sensing and sensor streams, and high-resolution images at a scale that exceeds the capacity of conventional statistical workflows. Artificial intelligence (AI) and machine learning (ML) offer a set of tools for extracting predictive signal from such data. This review surveys the application of ML and deep learning across the breeding cycle, beginning with the foundations of the main algorithm families and moving to genomic selection, where linear mixed models remain the benchmark against which random forests, support vector machines, neural networks and convolutional architectures are measured. It then considers image-based high-throughput phenotyping and disease detection, modelling of genotype-by-environment interaction and integration with crop growth models, yield prediction, optimisation of training populations and trial design, and the coupling of predictive models with genome editing. Deep learning is most firmly established for image- and sensor-based phenotyping, although generalisation across sites remains a recognised weakness, whereas the advantage of ML over well-specified linear models for genomic prediction of complex traits is modest and context dependent. Key obstacles include small sample sizes relative to the number of predictors, limited model interpretability, data heterogeneity, and unequal access to computing and expertise.