Accurate assessment of fish freshness remains a major challenge in the food industry, with direct consequences for product quality, market value, and consumer health. Conventional sensory evaluation is inherently subjective, inconsistent, and difficult to standardize across contexts, often limited by subtle, species-dependent spoilage cues. To address these limitations, we propose a handcrafted feature-based approach that systematically extracts and incrementally fuses complementary descriptors, including color statistics, histograms across multiple color spaces, and texture features such as Local Binary Patterns (LBP) and Gray-Level Co-occurrence Matrices (GLCM), from fish eye images. Our method captures global chromatic variations from full images and localized degradations from ROI segments, fusing each independently to evaluate their effectiveness in assessing freshness. Experiments on the Freshness of the Fish Eyes (FFE) dataset demonstrate the approach's effectiveness: in a standard train-test setting, a LightGBM classifier achieved 77.56% accuracy, a 14.35% improvement over the previous deep learning baseline of 63.21%. With augmented data, an Artificial Neural Network (ANN) reached 97.49% accuracy, surpassing the prior best of 77.3% by 20.19%. These results demonstrate that carefully engineered, handcrafted features, when strategically processed, yield a robust, interpretable, and reliable solution for automated fish freshness assessment, providing valuable insights for practical applications in food quality monitoring.

翻译：鱼类新鲜度的准确评估仍是食品工业面临的主要挑战，直接影响产品质量、市场价值和消费者健康。传统的感官评价方法本质上是主观的、不一致的，且难以在不同情境下标准化，其局限性常表现为难以捕捉物种依赖性的细微腐败特征。为克服这些不足，本研究提出一种基于手工特征的方法，系统性地从鱼眼图像中提取并增量融合互补描述符，包括多色彩空间下的颜色统计量、直方图，以及局部二值模式（LBP）和灰度共生矩阵（GLCM）等纹理特征。该方法从完整图像中捕获全局色彩变化，并从感兴趣区域（ROI）片段中提取局部劣化特征，通过独立融合评估各特征在新鲜度判别中的有效性。在鱼类眼睛新鲜度（FFE）数据集上的实验验证了该方法的有效性：在标准训练-测试设置下，LightGBM分类器取得了77.56%的准确率，较先前基于深度学习的基准方法（63.21%）提升了14.35%；使用增强数据后，人工神经网络（ANN）达到了97.49%的准确率，较先前最佳结果（77.3%）提升了20.19%。这些结果表明，经过精心设计和策略性处理的手工特征能够为自动化鱼类新鲜度评估提供鲁棒、可解释且可靠的解决方案，为食品质量监控的实际应用提供了重要参考。