Early-stage frost damage in citrus fruits is difficult to detect because external symptoms are often weak or absent, hindering intelligent robotic sorting in postharvest scenarios. To address this challenge, this study proposes a robotic multimodal tactile sensing approach inspired by human mechanoreception for frost-damage detection during grasping. A robotic gripper equipped with a 6×6 pressure matrix sensor and a piezoelectric vibration sensor was used to capture complementary tactile cues during standardized fruit handling, enabling the perception of subtle mechanical changes associated with early frost injury. Using 240 Citrus reticulata `Hong Mei Ren’ fruits under controlled experimental conditions, a Transformer-based multimodal fusion network was developed to jointly model pressure and vibration sequences for binary classification of normal and frost-damaged fruits. Across repeated stratified random-split experiments, the proposed method achieved a mean classification accuracy of 93.1%. Comparative experiments showed that the fusion model outperformed representative sequence-learning baselines, and ablation analysis confirmed that pressure–vibration fusion was more effective than either single modality alone. Attention-based temporal attribution further revealed that the most informative cues were concentrated in the initial contact and early loading stages, indicating the importance of early transient mechanical responses for frost-damage discrimination. Overall, the proposed approach demonstrates the feasibility of grasp-based robotic frost-damage detection under controlled experimental conditions.
Yu et al. (Tue,) studied this question.