AI-Powered Produce Inspection System

A client in the agri-tech sector needed to improve the way fresh produce was inspected before packaging. Manual inspection was:

Slow and inconsistent, relying on human judgment.

Error-prone, leading to good items being discarded and defective ones slipping through.

Difficult to scale, especially during seasonal peaks when labor shortages were common.

🔹 Solution

I developed a TensorFlow-based computer vision system that uses standard cameras to capture images of produce moving along the sorting line. The workflow included:

Image Classification Model → A custom CNN trained to detect shape irregularities, color changes, and visible defects.

Preprocessing Pipeline → Automated cropping, resizing, and lighting normalization to ensure accuracy regardless of camera placement.

Integration with Sorting Equipment → Once classified, items were automatically routed:

Grade A → Sent to premium packaging.

Grade B → Redirected for secondary processing.

Rejects → Flagged for removal.

🔹 Impact

✅ Consistency: Achieved over 95% accuracy in defect detection, outperforming manual inspection. ✅ Efficiency: Reduced inspection time by 70%, enabling faster throughput. ✅ Cost Savings: Lowered labor costs by reducing dependency on manual sorters. ✅ Scalability: System can easily be retrained to handle different produce types (fruits, vegetables, or packaged goods).

🔹 Tech Stack

TensorFlow / Keras → Custom CNN model training & deployment.

OpenCV → Image preprocessing & feature extraction.

TensorFlow Lite → Edge deployment on low-cost devices for real-time classification.

👉 Result: Delivered a scalable AI-powered quality control system that transformed produce inspection from a manual bottleneck into a streamlined, data-driven process.