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菜XAI-Rec:一个多阶段可解释的AI框架,用于菜的新鲜度分类和消费者建议
Akella S Narasimha Raju1, G Sujatha2, Ranjit Kumar Gatla3
1Department of Computing Technologies, School of Computing, College of Engineering & Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India. akellar@srmist.edu.in.
Scientific reports
|October 14, 2025
概括
本研究介绍了SpinachXAI-Rec,这是一种使用深度学习自动分类菜新鲜度的AI框架. 它为安全消费提供可解释的AI建议,增强食品安全.
科学领域:
- 计算机科学 计算机科学
- 食品科学 食品科学 食品科学
- 人工智能的人工智能
背景情况:
- 菜营养丰富,但很容易腐烂,导致腐烂和健康风险.
- 传统的新鲜度评估方法是主观的,耗时的,不可靠的.
- 消费的错误决定会影响人类健康和食品安全.
研究的目的:
- 开发一个自动化的,人工智能驱动的框架 (SpinachXAI-Rec) 用于分类菜的新鲜度.
- 为菜提供可解释的AI和可操作的消费者建议.
- 通过准确的新鲜度验证,提高食品安全.
主要方法:
- 创建了一个数据集,包含三个品种和六个类别 (新鲜/非新鲜) 的12,000个菜图像.
- 进行深度学习模型 (DenseNet121,ResNet50,EfficientNetB0) 的培训和评估.
- 集成了可解释的AI技术 (GradCAM++,LIME) 和基于规则的推系统.
主要成果:
- 在第一阶段,DenseNet121实现了96%的分类准确性.
- 组合的DenseNet121 + ViT-B/16 + SVM模型实现了0.97的F1得分和高分段精度 (Dice 0.89,IoU 0.82).
- 该框架成功地将菜分为"可食用"",可以谨慎食用"或"不可食用".
结论:
- 菜XAI-Rec为菜的新鲜度评估提供了一个准确和可解释的AI解决方案.
- 该系统使消费者和行业利益相关者能够做出明智,健康意识的决策.
- 这一进步通过人工智能驱动的验证和建议,有助于更安全的食品系统.
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