Automated Landmark-Based Root Inoculation in <i>Arabidopsis</i> Using Computer Vision and Robotics

Manual inoculation of plant roots is labor–intensive, spatially imprecise, and limits experimental throughput in plant–microbe interaction studies. Here, we present an integrated computer vision and robotics pipeline for automated, landmark-based root inoculation in Arabidopsis thaliana . Seedlings grown on Gelrite plates were imaged using the HADES automated phenotyping platform at the Netherlands Plant Eco–Phenotyping Centre, Utrecht University. A U–Net–based segmentation model (RootNet, F1 = 0.80) identified root structures, from which primary root tips were localized with a mean error of 0.25 mm. An affine coordinate transformation (mean target registration error: 1.09 mm) mapped image coordinates to the workspace of an Opentrons OT–2 liquid handling robot for targeted dispensing of 10 μL volumes. The system achieved successful inoculation in all 17 benchmark seedlings, corresponding to 100% accuracy (95% CI: 80–100%, Clopper-Pearson), and biological validation with fluorescent bacteria confirmed successful colonization along the root axis in 9 of 10 seedlings. To our knowledge, this is the first reported demonstration of automated, landmark-based root inoculation, extending the concept of automated phenotyping from passive measurement to active robotic intervention in real–time. The pipeline is generalizable to other root landmarks and organisms, enabling precise and reproducible delivery of microorganisms to specific root locations for systematic investigation of localized plant–microbe interactions.