Improving Fruit Quality with Banana Defect Detection on Matrice
Bananas are one of the most consumed fruits globally, making quality control during post-harvest processing essential. Detecting defects like bruises, cuts, black spots, and rot is critical for ensuring shelf-life, customer satisfaction, and export quality.
Using Matrice’s no-code AI platform, we developed a banana defect detection system powered by YOLOv9 to automate the quality inspection process.
This blog explains the steps involved in building this AI-based solution:
Dataset Preparation
Dataset Annotation
Model Training
Model Inference
Model Deployment
Dataset Preparation
The dataset consists of annotated images of bananas showing various defect types:
Bruises
Black Spots
Peel Damage
Mold or Rot
Total Images: 7,546
Training Set: 5,264 images
Validation Set: 1,525 images
Test Set: 757 images
Each image includes bounding box labels for defect regions.
Model Training
A YOLOv9 object detection model was trained to recognize multiple defect categories on bananas.
Batch Size: 16
Epochs: 50
Learning Rate: 0.001
Optimizer: Auto
Momentum: 0.95
Weight Decay: 0.0005
The model demonstrates robust performance in real-world scenarios such as packing stations or sorting lines.
Model Inference
The model can be exported in formats like:
PyTorch (.pt)
ONNX
TensorRT
OpenVINO
This allows seamless integration into edge-based visual inspection systems or cloud-based analytics platforms.
Model Deployment
With Matrice, the deployment process becomes effortless. Users can:
Run real-time detection in sorting or packaging lines
Visualize results on a dashboard
Trigger alerts for defective produce
Use Cases:
Post-harvest fruit grading in banana processing units
Automated rejection of defective fruits
Quality tracking for export batches
Conclusion
AI-powered defect detection helps reduce fruit wastage, ensure consistent quality, and streamline operations. Matrice allows agri-tech teams to deploy high-performing models without writing code—speeding up innovation in food quality management.
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