Integrating Digital Phenotyping, Automation, Robotics, and AI in Woody Plant Micropropagation

Le résumé fourni par la source

Woody plant micropropagation is undergoing rapid technological change driven by advances in digital phenotyping, robotics, and machine learning (ML). Traditional micropropagation, which is manual, labor-intensive, and highly dependent on operator expertise, faces persistent challenges, including high labor costs, genotype-dependent responses, physiological disorders, slow growth, and limited scalability. Digital phenotyping has become a central pillar of automation. Optical systems such as RGB, hyperspectral, multispectral, fluorescence, 2.5D/3D imaging, and X-ray computed tomography enable non-destructive, high-throughput monitoring of plant growth and physiology. These multimodal data streams feed ML models for early stress detection, embryo-quality prediction, and closed-loop control of culture conditions. Imaging through closed vessels, however, remains technically challenging due to condensation, reflections, and lighting limitations. Robotics is progressing from prototypes to operational platforms capable of aseptic subculturing, shoot cutting, and vessel manipulation. Although commercial adoption remains limited, successful implementations, particularly in conifer somatic embryogenesis, demonstrate strong potential for reducing labor requirements and improving reproducibility. ML and evolutionary algorithms represent a major conceptual shift from traditional optimization methods such as One-Factor-At-a-Time (OFAT) and Response Surface Methodology (RSM), which explore only small portions of the multidimensional culture space and often yield only a local “laboratory optimum.” ML models can capture nonlinear interactions, optimise multiple objectives simultaneously, and generate genotype-specific protocols, forming the computational foundation for autonomous micropropagation systems. The integration of AI, digital phenotyping, automation, and robotics is transforming plant cell, tissue, and organ culture from a labor-intensive craft into a standardized and data-driven biotechnology platform. By modelling the complex, nonlinear interactions among culture type, media composition, and environmental conditions, ML, particularly when coupled with evolutionary algorithms, enable predictive optimization and the identification of Pareto-efficient solutions that balance growth, proliferation, and physiological quality. This predictive capacity supports continuous quality control, genotype-specific decision-making, and real-time adjustments in robotic platforms and temporary immersion bioreactors (TIBs), accelerating protocol establishment at scale. Despite rapid progress, industrial adoption is constrained by limited datasets, reliance on licensed AI and proprietary technologies, and the lack of harmonized data frameworks. Advancing open-access platforms and interoperable data standards will be essential for deploying intelligent, resilient, and genotype-specific propagation systems for woody horticulture and clonal forestry.

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