Engineering evaluation demonstrates automated apple harvesting using tractor-mounted dual manipulators and vision systems, suggesting viable low-cost orchard automation.
The architecture of apple orchards can be converted to easier operation by implementing the use of a low‐cost, tractor‐mounted, dual manipulator based on a robotic harvesting system with lower degrees of freedom (DoF). To accurately avoid injuries by detecting apples, an efferent, real‐time vision system that includes a new gripping mechanism was developed. In this context, by introducing a new gripping system, spindle wall‐type apple orchard architectures have high potential for easy harvesting using a robotic platform. Therefore, this study was conducted to design and develop a tractor‐mounted, machine vision‐based, 3‐DoF, dual telescopic robotic cartesian system with a novel 2‐DoF gripping mechanism for apple harvesting. The machine vision system was developed using the EfficientDet object detection deep neural network with a mAP@0.5 of 0.775. The tracking algorithm DeepSORT was selected to track the localized apples and transfer the G ‐code to the robotic arm system. The developed novel gripping mechanism consists of three vacuum‐powered, (400 mmHg) spring‐loaded, silicone fingers that provide 0.73 N.m (±0.1) twisting torque to apples in a natural orientation (90°) and a dragging force of 17.65 N (±0.5). The average harvesting cycle time was approximately eleven seconds for both robotic arms, including detection and completion of the harvesting cycle.
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Abeyrathna et al. (2026) studied this question.
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