Integrating AI in plant science: A review of applications and future prospects

Plant science, which includes crop biology, genetics, and agronomy, is crucial for ensuring food security and enhancing agricultural productivity. As global food demand increases, the field is evolving by incorporating advanced technologies to address challenges such as climate change, disease resistance, and yield improvement. Artificial Intelligence is a key technology driving this transformation, offering new opportunities for innovation and progress in plant science. This review provides a comprehensive overview of the current and future applications of AI in plant science, with a special focus on areas where conventional techniques fall short. Unlike traditional methods that often rely on manual, time-intensive analysis, AI-driven models can learn complex patterns from high-dimensional biological and phenotypic data, automate decision-making, and scale rapidly. It begins with a discussion of the core principles of plant science, followed by an examination of AI technologies and their potential. The paper explores AI's role in plant genomics and breeding, focusing on key areas like genome sequencing, genetic marker identification, and the development of improved crop varieties. Special attention is given to AI-driven approaches in crop improvement, where machine learning models are increasingly used to optimize breeding programs, enhance yield predictions, support phenotypic selection, and address challenges like disease resistance. The review also discusses the challenges of applying AI in plant science, including issues with data quality, model interpretability, and integrating AI into large-scale agricultural practices. Finally, the paper looks ahead to the future of AI in plant science, suggesting directions for further research and development.