Intelligent Mechanical Characterization of Date Fruits for Automated Harvesting Grippers

Abstract

Robotic harvesting of date fruits requires precise grasping force control to prevent tissue damage, yet cultivar-specific biomechanical limits remain absent from the literature. This work presents the first continuous stress-strain characterization of three Saudi date cultivars across three hydration states, translated into validated robotic grasping constraints. A custom parallel-plate compression system emulates two-finger robotic grasping, while a Mask R-CNN vision model provides non-contact geometric measurement with below 5% relative error. Total of 500 samples are tested. Cyclic loading experiments establish elastic strain limits, with conservative operational thresholds of 7% for Ajwa and Barhi and 5% for Sagai. A linear calibration model maps gripper displacement to induced fruit strain, enabling strain-controlled robot commands. Validation using a UR10e manipulator confirms damage-free manipulation at these limits across all cultivars, with residual deformation below 1mm and strain tracking error below 1%. Future work will integrate vision and force feedback to train machine learning models on these experimentally derived limits, enabling real-time geometry-based gripper control for fully autonomous harvesting.