🤖 AI Summary
This study addresses the challenge of cephalothorax-abdomen separation in soft-shell shrimp post-harvest, which compromises visual quality and consumer acceptance. Traditional manual sorting suffers from low efficiency, poor consistency, and an inability to preserve freshness. To overcome these limitations, this work proposes the first application of deep learning–based image recognition for real-time automated classification of soft-shell shrimp immediately after harvest. A convolutional neural network (CNN) model, integrated with computer vision techniques, enables high-accuracy, automated quality assessment. The proposed method significantly improves classification accuracy and processing throughput while reducing reliance on manual labor, thereby minimizing handling time and better preserving product freshness. This approach advances the intelligent transformation of aquatic product processing and supports industry efforts to meet growing demand for high-quality seafood.
📝 Abstract
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.