Toward Reliable Vision-Guided Suction-and-Cut for Robotic Leaf Sampling in Arabidopsis Plants

Automated leaf sampling is essential for high-throughput genotype-phenotype mapping but remains challenging for small plants, such as Arabidopsis thaliana, that have dense canopies, tiny leaves (6~10mm) and slender petioles (0.8~1 mm). Such morphological traits demand high positional accuracy and specialized manipulation to reliably isolate individual leaves. To address these challenges, we present a fully automated robotic sampling system that combines a suction-cuttingend-effector with a two-stage vision framework for coarse-to-fine cutting-point alignment. The end-effector is driven by a non-interfering four-bar linkage, enabling compact motion and minimal disturbance to surrounding foliage. The control strategy iteratively replans motions based on local camera feedback until high-confidence alignment is achieved. Experiments across varied growth stages of Arabidopsis demonstrate high success rates with minimal disturbance, while ablation studies confirm the effectiveness of the selected perception methods. The system offers a reliable and precise automatic solution for plant genotype-phenotype mapping and broader laboratory automation workflows.