Balım Caner, Olgun Nevzat, Çalışan Mücahit
Department of Software Engineering, Faculty of Engineering, Afyon Kocatepe University, Afyonkarahisar 03200, Türkiye.
Department of Computer Engineering, Faculty of Engineering and Architecture, Bingöl University, Bingöl 12000, Türkiye.
Sensors (Basel). 2025 Jul 12;25(14):4374. doi: 10.3390/s25144374.
Fish is important for human health due to its high nutritional value. However, it is prone to spoilage due to its structural characteristics. Traditional freshness assessment methods, such as visual inspection, are subjective and prone to inconsistency. This study proposes a novel, cost-effective hybrid methodology for automated three-level fish freshness classification (Day 1, Day 2, Day 3) by integrating single-wavelength laser reflectance data with deep learning-based image features. A comprehensive dataset was created by collecting visual and laser data from 130 mackerel specimens over three consecutive days under controlled conditions. Image features were extracted using four pre-trained CNN architectures and fused with laser features to form a unified representation. The combined features were classified using SVM, MLP, and RF algorithms. The experimental results demonstrated that the proposed multimodal approach significantly outperformed single-modality methods, achieving average classification accuracy of 88.44%. This work presents an original contribution by demonstrating, for the first time, the effectiveness of combining low-cost laser sensing and deep visual features for freshness prediction, with potential for real-time mobile deployment.
由于其高营养价值，鱼类对人类健康很重要。然而，由于其结构特征，它很容易变质。传统的新鲜度评估方法，如目视检查，具有主观性且容易出现不一致性。本研究提出了一种新颖的、具有成本效益的混合方法，通过将单波长激光反射率数据与基于深度学习的图像特征相结合，对鱼类新鲜度进行自动三级分类（第1天、第2天、第3天）。通过在受控条件下连续三天从130个鲭鱼样本中收集视觉和激光数据，创建了一个综合数据集。使用四种预训练的卷积神经网络（CNN）架构提取图像特征，并与激光特征融合，形成统一表示。使用支持向量机（SVM）、多层感知器（MLP）和随机森林（RF）算法对组合特征进行分类。实验结果表明，所提出的多模态方法明显优于单模态方法，平均分类准确率达到88.44%。这项工作首次证明了结合低成本激光传感和深度视觉特征进行新鲜度预测的有效性，具有实时移动部署的潜力，做出了原创性贡献。
