An Edge-Ready Lightweight Computer Vision Framework for On-Site Fish Disease Detection in Aquaculture Management

Efficient detection of fish diseases is essential for intelligent health monitoring and timely intervention in aquaculture. However, current computer vision models remain computationally intensive, hindering their deployment on resource-constrained edge devices in aquaculture applications. To this end, this study developed a lightweight detection framework based on an improved You Only Look Once (YOLO), aiming to achieve a favorable balance between detection accuracy and on-site inference efficiency. First, a Dual-Branch Feature-Preserving Downsampling (DFPD) module was proposed to enhance the extraction of valuable disease-related cues with minimal computational overhead. Subsequently, structured pruning was applied to compress the optimized baseline model. Four pruning techniques, including Slim, GroupTaylor, Layer-Adaptive Magnitude-Based Pruning (LAMP), and L1-based, were evaluated under the same conditions. The enhanced baseline model improved precision from 0.864 to 0.908 and mAP@0.5:0.95 from 0.613 to 0.632, while already reducing the Number of Parameters (Params) and Giga Floating-point Operations Per Second (GFLOPs) compared with the original YOLOv8n. Among the pruning techniques, L1-based produced the best overall trade-off, yielding a final model that maintained a F1-score of 0.860 while reducing Params and GFLOPs by 54.7% and 49.4%, respectively, relative to the original detector. Ablation studies further revealed that a moderate FLOPs reduction of approximately 41% to 47% was optimal for preserving diagnostic performance while enhancing compactness. Edge deployment tests on an RK3588S device verified the framework’s practical inference speed advantage. Therefore, this study offers a deployment-friendly computer vision solution for on-site fish disease detection in aquaculture management, particularly suited to real-world scenarios with limited computational resources.