Multimodal methods are widely used in rice deterioration detection, which exhibit limited capability in representing and extracting fine-grained abnormal features. Moreover, these methods rely on devices, such as hyperspectral cameras and mass spectrometers, increasing detection costs and prolonging data acquisition time. To address these issues, we propose a feature recalibration based olfactory-visual multimodal model for fine-grained rice deterioration detection. The fine-grained deterioration embedding constructor (FDEC) is proposed to reconstruct the labeled multimodal embedded-feature dataset, enhancing sample representation. The fine-grained deterioration recalibration attention network (FDRA-Net) is proposed to emphasize signal variations and increase sensitivity to fine-grained deterioration on the rice surface. Experiments show that the proposed method achieves a classification accuracy of 99.89%. Compared with state-of-the-art methods, the detection accuracy is improved and the procedure is simplified. Furthermore, field detection demonstrates the advantages of accuracy and operational simplicity. The proposed method can also be extended to other agrifood in agriculture and food industry.

翻译：多模态方法在大米变质检测中应用广泛，但这些方法在表征和提取细粒度异常特征方面能力有限。此外，这些方法依赖高光谱相机和质谱仪等设备，增加了检测成本并延长了数据采集时间。为解决这些问题，我们提出了一种基于特征重校准的嗅觉-视觉多模态模型，用于细粒度大米变质检测。我们提出了细粒度变质嵌入构造器（FDEC）来重建带标签的多模态嵌入特征数据集，以增强样本表征能力。同时，提出了细粒度变质重校准注意力网络（FDRA-Net），以强调信号变化并提高对大米表面细粒度变质的敏感性。实验表明，所提方法的分类准确率达到99.89%。与现有先进方法相比，检测精度得到提升且流程得以简化。此外，现场检测验证了该方法在准确性和操作简便性方面的优势。所提方法还可扩展应用于农业和食品工业中的其他农产品。