Intelligent System for Monitoring Shrimp Farming Ponds

Shrimp farming is a strategic productive activity for Ecuador; however, pond inspection still substantially depends on manual observation and fragmented visual records. This study describes a prototype mobile/web architecture for image capture, storage, and result visualization and, as a separate experiment, evaluates supervised multiclass instance segmentation on public proxy data. A unified dataset of 4508 images was constructed from three external sources using the classes foam, floater, and shrimp. YOLOv8s-seg was used as the internal reference baseline and YOLOv11s-seg as the comparison candidate; both were trained under the same configuration and evaluated on 445 test images. YOLOv8s-seg achieved mAP50 values of 0.678 for BOX and 0.621 for MASK, whereas YOLOv11s-seg achieved 0.677 and 0.616, respectively. Their isolated GPU inference times were 10.4 and 10.2 ms/img. A weighted global experimental performance index comprising 90% predictive quality and 10% inference efficiency reached 0.596 and 0.593, respectively. Both models performed strongly for floater and shrimp, whereas foam showed low recall and zero MASK mAP50 because of the small number of positive test images and heterogeneous annotations. The evaluated task is supervised segmentation of proxy visual categories rather than anomaly detection in its conventional methodological sense, and no general improvement or practical superiority of one architecture was demonstrated. The study does not calibrate an operational confidence threshold or alert-persistence rule, implement adaptive or online learning, validate images from Ecuadorian production ponds, deploy the unified detector within the API, or demonstrate end-to-end real-time monitoring. Consequently, the results constitute a controlled experimental baseline and must not be interpreted as evidence of operational performance or local-domain generalization.