A Robotic Picking Framework for Autonomous Singulation of Sweetpotato Slips into Chainpots

Labor shortages in agriculture, particularly for tasks requiring high dexterity such as sweetpotato transplanting, have increased the need for robotic automation. Although automated transplanting machines are available, they require manually singulated slips to be loaded into chainpots, making the overall process labor-intensive and costly. To address this limitation, this work investigates the feasibility and challenges of a robotic framework for automating the slip singulation task in chainpots. The proposed framework integrates a general-purpose robotic manipulator, a 3D camera, and a parallel-jaw gripper. The perception system consists of two core modules: (1) a deep learning-based instance segmentation and keypoint detection model for localizing individual slips and estimating their transplanting direction, and (2) a simple analytical grasp-planning algorithm for computing stable and collision-free grasp poses. To overcome the limited availability of annotated training data, a novel data generation pipeline is developed to create a large synthetic annotated dataset from a small collection of real-world images. The proposed pipeline generates photorealistic cluttered scenes by randomly compositing annotated top-down images of individual slips onto a target background while simultaneously applying domain randomization. During real-world experiments conducted under controlled laboratory conditions, the robotic framework achieved a grasp success rate of 72-100% and a transplanting success rate of 75-88%, depending on the degree of slip overlap, occlusion, and leaf entanglement. The proposed framework represents an initial important step toward the development of a fully automated robotic system for sweetpotato slip transplanting.