OTDA: Octopus-Inspired Therapeutic Decision Agent Based on Using MS-YOLOv11n Detection for Fish Disease Treatment

Timely and accurate identification of fish diseases, along with the provision of corresponding treatment plans, is crucial for improving fish welfare and reducing economic losses in aquaculture facilities. To address issues such as low detection efficiency and excessive reliance on manual experience in aquaculture, this paper proposes an innovative diagnostic and treatment method based on the Octopus framework for model optimization. The CBAM (Convolutional Block Attention Module) attention mechanism is introduced into the YOLOv11n backbone to enhance feature extraction, while the Focus-CIoU (Focus-Complete Intersection over Union) loss function is adopted to improve the detection performance for small-target lesions and dense fish schools. Additionally, by integrating wireless sensor networks and the OTDA (Octopus-inspired Therapeutic Decision Agent) bionic agent, an integrated ‘detection-identification-perception-recommendation’ framework is constructed. The results showed that the optimized YOLOv11n model achieved a detection precision of 98.00%, a recall of 94.86%, and an F1 score of 96.40% in detecting ulcer disease, tail rot disease, and red skin disease. Furthermore, the mAP@50 was 97.58%, and the mAP@50–95 was 85.68%. Compared with the traditional YOLOv11n model, the optimized model achieved improvements of 1.39%, 1.48%, 1.43%, 1.4%, and 2.74% in precision, recall, F1 score, mAP@50, and mAP@50–95, respectively. The treatment recommendations produced by means of the Octopus bionic intelligent agent resulted in an average system response time of no more than 53 s, with the maximum observed response time not exceeding 60 s. The identification accuracy of OTDA remained above 87.1%.