用深度学习自动识别软壳虾新鲜度，提升分选效率与品质一致性。
Deep Learning-Based Image Recognition for Soft-Shell Shrimp Classification
- 基于卷积神经网络实现捕捞后虾体快速分类
- 相比人工分拣准确率显著提升，处理时间大幅缩短
- 适合水产加工企业用于保鲜管理与自动化生产
随着信息技术融入水产养殖，产量持续稳定增长。消费者对高品质水产品的需求上升，新鲜度与外观完整性成为关键。在虾类加工品中，捕捞后新鲜度迅速下降，软壳虾常因烹饪或冷冻导致头身分离，影响外观和消费体验。为此，本研究采用基于深度学习的图像识别技术，对白虾捕捞后立即进行自动化分类。通过卷积神经网络（CNN）模型替代人工分拣，显著提升分类准确率、处理效率与结果一致性。该技术有效缩短处理时间，有助于保持虾体新鲜度，使虾类运输企业更高效满足客户需求。
原文摘要 · Abstract (English)
With the integration of information technology into aquaculture, production has become more stable and continues to grow annually. As consumer demand for high-quality aquatic products rises, freshness and appearance integrity are key concerns. In shrimp-based processed foods, freshness declines rapidly post-harvest, and soft-shell shrimp often suffer from head-body separation after cooking or freezing, affecting product appearance and consumer perception. To address these issues, this study leverages deep learning-based image recognition for automated classification of white shrimp immediately after harvest. A convolutional neural network (CNN) model replaces manual sorting, enhancing classification accuracy, efficiency, and consistency. By reducing processing time, this technology helps maintain freshness and ensures that shrimp transportation businesses meet customer demands more effectively.
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