Vision-Based Early Fault Diagnosis and Self-Recovery for Strawberry Harvesting Robots

February 09, 2026

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📝 Original Info

Title: Vision-Based Early Fault Diagnosis and Self-Recovery for Strawberry Harvesting Robots
ArXiv ID: 2601.02085
Date: 2026-01-05
Authors: Meili Sun, Chunjiang Zhao, Lichao Yang, Hao Liu, Shimin Hu, Ya Xiong

📝 Abstract

Strawberry harvesting robots faced persistent challenges such as low integration of visual perception, fruit-gripper misalignment, empty grasping, and strawberry slippage from the gripper due to insufficient gripping force, all of which compromised harvesting stability and efficiency in orchard environments. To overcome these issues, this paper proposed a visual fault diagnosis and self-recovery framework that integrated multi-task perception with corrective control strategies. At the core of this framework was SRR-Net, an end-to-end multi-task perception model that simultaneously performed strawberry detection, segmentation, and ripeness estimation, thereby unifying visual perception with fault diagnosis. Based on this integrated perception, a relative error compensation method based on the simultaneous target-gripper detection was designed to address positional misalignment, correcting deviations when error exceeded the tolerance threshold. To mitigate empty grasping and fruit-slippage faults, an early abort strategy was implemented. A micro-optical camera embedded in the end-effector provided real-time visual feedback, enabling grasp detection during the deflating stage and strawberry slip predic...

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Vision-Based Early Fault Diagnosis and Self-Recovery for Strawberry Harvesting Robots

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