A Soft Robotic Gripper for Crop Harvesting: Prototyping, Imaging, and Model-Based Control

The global agricultural sector faces escalating labor shortages and post-harvest losses, particularly in delicate crop handling. This study introduces an integrated soft robotic harvesting system addressing these challenges through four key innovations. First, a low-cost, high-yield fabrication method for silicone-based soft grippers is proposed, reducing production costs by 60% via compressive-sealing molds. Second, a decentralized IoT architecture with edge computing achieves real-time performance (42 fps to 73 fps) on affordable hardware (around $180 per node). Third, a lightweight vision pipeline combines handcrafted geometric features and contrast analysis for crop maturity assessment and gripper tracking under occlusion. Fourth, a Neo-Hookean-based statics model incorporating circumferential stress and variable cross-sections reduces tip position errors to 5.138 mm. Experimental validation demonstrates 100% gripper fabrication yield and hybrid feedforward–feedback control efficacy. These advancements bridge the gap between laboratory prototypes and field-deployable solutions, offering scalable automation for perishable crop harvesting.