Advances in Genetics and Plant Breeding with Artificial Intelligence, Data Science and Machine Learning

Abstract

The integration of Artificial Intelligence (AI) in agriculture is revolutionizing genetics and plant breeding. Traditional breeding methods, while successful, are often time-consuming and resource-intensive. Recent advances in AI, particularly machine learning and deep learning, provide powerful tools for analyzing massive genomic datasets, predicting desirable traits, and accelerating the breeding cycle. AI-driven platforms are being used to interpret genetic variability, identify marker-trait associations, forecast yield performance under climate stress, and optimize hybrid selection. This article highlights how AI is reshaping modern plant breeding and genetics, making crop improvement more precise, efficient, and sustainable. Artificial Intelligence in plant breeding is largely powered by data science (DS) and machine learning (ML). These two fields provide the mathematical and computational framework that enables breeders to handle vast datasets, recognize patterns, and make predictions. Plant breeding has entered the “big data era,” where genomic sequences, phenotypic images, climate records, soil profiles, and management practices all generate enormous volumes of information. Making sense of this complexity requires robust data science pipelines and intelligent algorithms.