Feature Recalibration Based Olfactory-Visual Multimodal Model for Fine-Grained Rice Deterioration Detection

2026-02-16

AI Summary

This study addresses the limitations of existing rice deterioration detection methods, which struggle to effectively capture fine-grained anomalous features and rely on costly equipment, resulting in low efficiency and high expenses. To overcome these challenges, this work proposes a lightweight olfactory-visual multimodal detection framework that innovatively incorporates a fine-grained deterioration embedding constructor to reconstruct labeled data and introduces a recalibrated attention network to enhance sensitivity to subtle deterioration signals. The proposed method eliminates the need for expensive instrumentation and achieves a classification accuracy of 99.89% on a self-constructed dataset, significantly outperforming current state-of-the-art approaches. Field experiments further demonstrate its high precision, ease of deployment, and potential for extension to other agricultural products.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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 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.

Problem

• fine-grained deterioration
• multimodal detection
• rice deterioration
• feature representation
• detection cost

Innovation

• feature recalibration
• olfactory-visual multimodal
• fine-grained deterioration detection
• FDRA-Net
• FDEC