Open Access

ARTICLE

A Novel Approach Based on Graph Attention Networks for Fruit Recognition

Dat Tran-Anh¹, Hoai Nam Vu^2,3,*

1 Faculty of Information Technology, Thuyloi University, Ha Noi, 100000, Viet Nam
2 Faculty of Artificial Intelligence, Posts and Telecommunications Institute of Technology, Nguyen Trai, Ha Noi, 100000, Viet Nam
3 Young Innovation Research Laboratory on Digital Technology (YIRLoDT), Posts and Telecommunications Institute of Technology, Nguyen Trai, Ha Noi, 100000, Viet Nam

* Corresponding Author: Hoai Nam Vu. Email:

Computers, Materials & Continua 2025, 82(2), 2703-2722. https://doi.org/10.32604/cmc.2025.061086

Received 16 December 2024; Accepted 02 January 2025; Issue published 17 February 2025

Abstract

Counterfeit agricultural products pose a significant challenge to global food security and economic stability, necessitating advanced detection mechanisms to ensure authenticity and quality. To address this pressing issue, we introduce iGFruit, an innovative model designed to enhance the detection of counterfeit agricultural products by integrating multimodal data processing. Our approach utilizes both image and text data for comprehensive feature extraction, employing advanced backbone models such as Vision Transformer (ViT), Normalizer-Free Network (NFNet), and Bidirectional Encoder Representations from Transformers (BERT). These extracted features are fused and processed using a Graph Attention Network (GAT) to capture intricate relationships within the multimodal data. The resulting fused representation is subsequently classified to detect counterfeit products with high precision. We validate the effectiveness of iGFruit through extensive experiments on two datasets: the publicly available MIT-States dataset and the proprietary TLU-States dataset, achieving state-of-the-art performance on both benchmarks. Specifically, iGFruit demonstrates an improvement of over 3% in average accuracy compared to baseline models, all while maintaining computational efficiency during inference. This work underscores the necessity and innovativeness of integrating graph-based feature learning to tackle the critical issue of counterfeit agricultural product detection.

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Keywords

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