Abstract
This study presents the design, development, and evaluation of an intelligent fruit-picking robot that integrates convolutional vision, adaptive gripping mechanisms, and kinematic control to enable automated harvesting in diverse orchard environments. The proposed system combines a dual-manipulator platform with an extendable scissor-lift mechanism to achieve wide workspace coverage, allowing efficient access to fruits located at varying canopy heights. A deep learning-based recognition module, trained on a Mixed Fruit Dataset, is employed to detect and classify fruits under challenging conditions characterized by occlusions, variable illumination, and dense foliage. Visualization of feature activations confirms that the model effectively focuses on discriminative fruit regions, supporting precise alignment of the end-effector during grasping. The adaptive gripper, designed with compliant materials and multi-configuration geometry, ensures gentle handling across fruits of different shapes and sizes, minimizing mechanical damage. Experimental evaluations demonstrate that the system performs reliably across multiple fruit species, achieving accurate identification, robust segmentation, and stable manipulation in real-field scenarios. The integrated results highlight the robot’s potential to reduce labor dependency, improve harvesting efficiency, and support scalable automation in mixed-crop orchards. Future work will address enhancements in real-time processing, autonomous navigation, and cross-species generalization to advance fully autonomous orchard operations.