Industry Context: Ensuring Consistent Seafood Quality

In the fast-paced world of seafood processing, even a single spoiled or damaged fish can mean health risks, rejected shipments, and costly reputational damage. Yet manual visual inspections struggle to keep up:

• Subtle defects like discoloration or tissue damage can be missed

• High-speed production lines overwhelm human inspectors

• Inspection accuracy varies by person and shift

• Manual checks create bottlenecks and raise labor costs

• Lack of structured data prevents traceability and continuous improvement

  
  

Seafood processors need scalable, reliable, and data-driven inspection workflows that catch every defect in real time.

Tuba.AI: Empowering Your Team to Build Smart Inspection Workflows

Tuba.AI is a no-code/low-code computer-vision platform that lets your quality, operations, and engineering teams craft end-to-end AI inspection pipelines in minutes. With intuitive drag-and-drop blocks.

With Tuba.AI, teams can:

• Auto-Label Defects & Species: Quickly annotate “good” vs. “spoiled” fish by species with built-in auto-labeling

• Train Custom Models: Choose your accuracy-vs-time trade-off, select PyTorch, and export in ONNX, OpenVINO, TensorRT, or quantized formats—no code required

• Deploy Anywhere: One-click deploy to edge devices on the production line or to cloud servers for centralized monitoring

• Monitor & Retrain Continuously: Track performance, review alerts, and update your model with new data as quality standards evolve

• Integrate Seamlessly: Plug trained models into your existing QC systems via standard APIs and model files

• Scale Instantly: Add new fish types, defect categories, or processing lines by uploading more data and hitting “Retrain”

Use Case Overview: AI-Powered Fish Quality Detection with Tuba.AI

To showcase Tuba.AI’s speed and flexibility, we built a Fish Quality Inspection workflow using a public  Fish quality detection dataset. This dataset includes images labeled by species (e.g., ilish, rui, tilapia) and quality (“good” or “bad”).

Key Steps Workflow on Tuba.AI:

1. Data Labeling:

Upload a labeled dataset containing fish images categorized by type and quality (e.g., good-ilish, good-rui, good-tilapia, bad-ilish, bad-rui, and bad-tilapia). This flexible labeling setup can be extended to other fish types or quality conditions. Select the dataset type "detection".

2. Model Training:

Select the training type as the same as the dataset type “detection”. The labeled dataset is used to train a quality inspection model within Tuba’s no-code environment. The training process is optimized to recognize each fish type and its quality (fresh or spoiled).

Alternatively, you can select manual operation and select the required parameters. 

1. Accuracy vs Time: define what the training process considers most during training.

2. Framework: The framework to use. Pytorch is the standard and the go-to choice because of its wide support.

3. OpenVino/TensorRT format: When selected in Framework, users get to choose to what degree quantization should be applied to the weights of the model; float 16, float 32, integer 8.

4. Accuracy: Users are allowed to choose which model to choose, the one with the best accuracy or the one with the optimum accuracy, choosing optimum will ensure that the model generalizes better.

5. Time: Users choose the maximum time to take during training.

   
   

3. Deployment:

Once trained, the model is deployed to edge devices or cloud infrastructure, allowing real-time inspection during production. Deploying the model is a critical step to run the model in production at high scale.

4. Inference and Performance Evaluation:

The deployed model analyzes incoming images to classify each fish as good or bad. Real-time alerts can be triggered for immediate removal of poor-quality items. Continuous monitoring and retraining help maintain performance.

5. Integration and Scalability:

The fish inspection workflow can be integrated into broader quality control systems. It can be expanded to include more species or additional defect types simply by updating the dataset and retraining the model.

Results of the Use Case

As shown in the below charts:

• Initial Performance: Model began training with a mean Average Precision (mAP) of 24%, indicating early learning across classes.

• Training Peak: Achieved 99% mAP during training, showing strong learning of visual patterns and defect characteristics.

• Final Validation: Reached a 96% validation mAP, confirming that the model generalized well and didn’t overfit the training data.

• Visual Testing: Sample test images demonstrated consistent, high-quality detection across multiple fish types and spoilage levels.

Business Impact of Using Tuba.AI Workflow

Ready to Build Smarter Fish Quality Workflows?

Elevate your seafood processing with AI-powered inspection that’s fast, reliable, and fully under your team’s control. 

Talk to our experts and learn how Tuba.AI can help you build AI Vision workflows faster, easier, and more cost-effectively.

Resources 

• Dataset Link:  Fish quality detection dataset