# Edge-Deployable Fish Feeding-State Quantification and Recognition via Frame-Pair Motion Encoding and EfficientFeedingNet

## TL;DR

This paper introduces a lightweight video-based system to detect when farmed fish are feeding, aiming to reduce waste and improve fish welfare in aquaculture.

## Contribution

The novel contribution is a motion-based, edge-deployable framework with EfficientFeedingNet for real-time feeding-state recognition in aquaculture.

## Key findings

- EfficientFeedingNet achieved 96.53% accuracy in feeding-state recognition.
- Models trained on automatically labeled data outperformed human-labeled datasets by 13.13–18.46 percentage points.
- The system runs at 143.24 fps on a Jetson Orin NX, enabling real-time deployment.

## Abstract

This study presents a lightweight video-based method to recognize when a school of farmed fish is actively feeding, helping farmers avoid overfeeding that can waste feed, degrade water quality, and compromise fish welfare. We recorded overhead videos of juvenile black rockfish in farm tanks and measured how movement changes between two nearby video frames. We turned these movement changes into a single color image that shows where fish move and how strongly they move. Using the overall movement strength over time, we automatically determined the period when fish respond to feed and created an automatically labeled set of Feeding and Non-feeding examples. We then compared several lightweight image-based computer models and developed a fast model named EfficientFeedingNet for real-time use on low-cost farm devices. Models trained with the automatic labels achieved over 90 percent accuracy and were 13.13 to 18.46 percentage points more accurate than models trained with labels made only by human observers; EfficientFeedingNet reached 96.53% accuracy. This approach can support smarter feeding decisions that may reduce waste and associated environmental impacts, with potential benefits for productivity and welfare in intensive aquaculture.

Accurate feeding-state monitoring is essential for improving feeding management, reducing feed waste, and supporting water quality and fish welfare in aquaculture. However, existing vision-based methods often rely on subjective labels or computationally expensive temporal models, which limits practical on-farm deployment. Here, we propose an objective, edge-deployable framework for motion-driven feeding-state quantification and binary feeding/non-feeding recognition from top-view videos. The framework integrates frame-pair dense optical-flow encoding with a lightweight network (EfficientFeedingNet) to enable real-time deployment. Using an optical-flow-derived motion-intensity signal (V-Value), we automatically delineate feeding-response intervals and construct a perception-based dataset (Perceptual Dataset) with reproducible binary labels, alongside an observer-labeled Intuitive Dataset. Across representative backbones, models trained on the Perceptual Dataset achieve >90% test accuracy and improve over the Intuitive Dataset by 13.13–18.46 percentage points. The proposed EfficientFeedingNet attains 96.53% test accuracy while remaining lightweight for edge deployment; on a Jetson Orin NX, it runs at 7.0 ms per image (143.24 fps). Overall, the proposed framework provides a practical basis for timely, data-driven feeding decisions in precision aquaculture.