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Integrative zero-shot learning for fruit recognition
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AI
DOI：10.1007/s11042-024-18439-x.png)
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In order to address the pressing issue of detecting and identifying counterfeit agricultural products, which cause significant economic losses, this paper presents a mobile-based system designed to identify rare agricultural products for consumers. To tackle the problem of identifying fake agricultural products, we propose a novel self-monitoring deep learning model named iCZSL. This model combines the Zero-shot segmentation model and Graph embeddings with the calculation of edit distance using a candidate list obtained from a dictionary. To evaluate the effectiveness of our approach, we conduct experiments on a dataset consisting of different types of potatoes, including chinese potatoes, dalat potatoes (a rare agricultural product), and other varieties, referred to as the TLU-states dataset. The experimental results demonstrate the superiority of our proposed method over competing baselines in terms of both quantitative and qualitative performance measures. This system holds promising potential for effectively detecting and differentiating rare agricultural products, thereby mitigating economic losses caused by counterfeit items.
Keyword:
Fruit recognition
Zero-shot learning
Graph convolutional networks
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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分享收藏Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning基于深度学习的果蔬新鲜度分类改进方法.png)
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