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[2602.14408] Feature Recalibration Based Olfactory-Visual Multimodal Model for Fine-Grained Rice Deterioration Detection
Summary
The paper presents a novel olfactory-visual multimodal model for detecting fine-grained rice deterioration, achieving high accuracy and simplifying detection processes.
Why It Matters
This research addresses the limitations of existing methods in rice deterioration detection, which often require expensive equipment and lengthy data collection. By improving accuracy and operational simplicity, this model has significant implications for agricultural practices and food quality management.
Key Takeaways
- Introduces a feature recalibration model for enhanced rice deterioration detection.
- Achieves a classification accuracy of 99.89%, outperforming existing methods.
- Simplifies the detection process, reducing reliance on costly equipment.
- Potential applications extend beyond rice to other agrifood products.
- Highlights the importance of multimodal approaches in agricultural technology.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.14408 (cs) [Submitted on 16 Feb 2026] Title:Feature Recalibration Based Olfactory-Visual Multimodal Model for Fine-Grained Rice Deterioration Detection Authors:Rongqiang Zhao, Hengrui Hu, Yijing Wang, Mingchun Sun, Jie Liu View a PDF of the paper titled Feature Recalibration Based Olfactory-Visual Multimodal Model for Fine-Grained Rice Deterioration Detection, by Rongqiang Zhao and 4 other authors View PDF HTML (experimental) Abstract: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 i...
