97.8% accurate potato defect detection using AI vision

AI-powered computer vision system that automatically detects, grades, and separates defective potatoes on high-speed conveyor belts, reducing manual inspection and improving product quality.

Challenge

Twin Forks Potato Company, an Idaho-based potato packing company, relied on manual visual inspection to identify defective potatoes before packaging. As production volumes increased, thousands of potatoes moved through the sorting line every hour, making it difficult for workers to consistently detect bruises, cuts, rot, greening, sprouting, and other surface defects.

Solution

We designed and deployed an AI-powered computer vision platform that performs real-time potato inspection directly on the production line. Using high-speed industrial cameras and deep learning models trained on thousands of annotated potato images, the system accurately identifies multiple defect types while maintaining production-line speed.

Results

97.8% Defect detection accuracy — Agriculture.

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