Abstract
Recent technological advances have produced vast and complex data sets for crop improvement and plant breeding, termed big data. The challenge arising from this plethora of information is to extract meaningful information to be applied for developing next-generation crops and advancing current agricultural practices for crop production. Artificial intelligence (AI) and machine learning (ML), a subfield of AI, offer a set of computational approaches that aim to transform the information from large-scale datasets into breeding decisions by bringing different data types together and identifying hidden patterns. Here, we present an overview of big data, ML and AI in the context of crop improvement. We discuss what big data is, the challenges arising from large-scale datasets, and the big data types encountered in crop improvement research. We introduce the basics of AI and ML, focusing on examples drawn from agriculture. We highlight the advantages and scopes for applying ML and AI in crop improvement and gain insight into how big data, ML, and AI are integrated into research presently. We show how combining big data with AI and ML can revolutionise decision-making processes in crop improvement.