Digital dimension of plant science research: A bird's eye view of AI integration

Plant science is undergoing a transformation through the integration of artificial intelligence (AI), high-throughput phenotyping, plant biotechnology and multi-omics, and smart digital technologies. The digitization of plant data from molecular signals to ecosystem-level observations has redefined plant science from descriptive biology to predictive, system-level, and design-oriented research domain. Inclusion of machine learning and deep learning enable genotype-phenotype alignment, precision farming, optimization of tissue culture systems, genome editing improvement, and biosynthetic pathway reconstruction, while integration with IoT, robotics, microfluidics, and digital twins support real-time plant growth monitoring and modelling. Besides supporting crop improvement programs and sustainable agriculture, AI-driven platforms contribute to biodiversity mapping and conservation. However, challenges including data standardization, infrastructure demands, model interpretability, and digital literacy gaps reduce the broader adoption of technology. This article presents an integrated overview of the AI-plant science platform and summaries key insights to help bridge these barriers and advance sustainable, data-driven plant research.