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A Novel Approach Based on Graph Attention Networks for fruit recognition

Computers, Materials & Continua 2025年第2期82卷 2703-2722页

作者： Dat Tran-Anh Hoai Nam Vu Faculty of Information Technology Thuyloi UniversityHa Noi100000Viet Nam Faculty of Artificial Intelligence Posts and Telecommunications Institute of TechnologyNguyen TraiHa Noi100000Viet Nam Young Innovation Research Laboratory on Digital Technology(YIRLoDT) Posts and Telecommunications Institute of TechnologyNguyen TraiHa Noi100000Viet Nam

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.

关键词： fruit recognition graph attention network multi-feature processing

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Fast and accurate detection of kiwifruits in the natural environment using improved YOLOv4

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 222-230,I0001页

作者： Jinpeng Wang Lei Xu Song Mei Haoruo Hu Jialiang Zhou Qing Chen Co-Innovation Center of Efficient Processing and Utilization of Forest Resources Nanjing Forestry UniversityNanjing 210037China School of Mechanical and Electronic Engineering Nanjing Forestry UniversityNanjing 210037China Nanjing Institute of Agricultural Mechanization Ministry of Agricultural and Rural AffairsNanjing210014China

Real-time detection of kiwifruits in natural environments is essential for automated kiwifruit harvesting. In this study, a lightweight convolutional neural network called the YOLOv4-GS algorithm was proposed for kiwifruit detection. The backbone network CSPDarknet-53 of YOLOv4 was replaced with GhostNet to improve accuracy and reduce network computation. To improve the detection accuracy of small targets, the upsampling of feature map fusion was performed for network layers 151 and 154, and the spatial pyramid pooling network was removed to reduce redundant computation. A total of 2766 kiwifruit images from different environments were used as the dataset for training and testing. The experiment results showed that the F1-score, average accuracy, and Intersection over Union (IoU) of YOLOv4-GS were 98.00%, 99.22%, and 88.92%, respectively. The average time taken to detect a 416×416 kiwifruit image was 11.95 ms, and the model’s weight was 28.8 MB. The average detection time of GhostNet was 31.44 ms less than that of CSPDarknet-53. In addition, the model weight of GhostNet was 227.2 MB less than that of CSPDarknet-53. YOLOv4-GS improved the detection accuracy by 8.39% over Faster R-CNN and 8.36% over SSD-300. The detection speed of YOLOv4-GS was 11.3 times and 2.6 times higher than Faster R-CNN and SSD-300, respectively. In the indoor picking experiment and the orchard picking experiment, the average speed of the YOLOv4-GS processing video was 28.4 fps. The recognition accuracy was above 90%. The average time spent for recognition and positioning was 6.09 s, accounting for about 29.03% of the total picking time. The overall results showed that the YOLOv4-GS proposed in this study can be applied for kiwifruit detection in natural environments because it improves the detection speed without compromising detection accuracy.

关键词： kiwifruits fruit recognition natural environments YOLOv4

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