Reducing Waste and Labor Costs in Food Retail: An Automated Quality Inspection Solution

Services Offered: Artificial Intelligence / Machine Learning

Industry: Retail

Problem Statement

Our client from the food retail industry sought our expertise in developing an automated AI solution to improve the quality inspection and waste management processes of their fruits and vegetables business. They wanted to optimize their existing manual processes and reduce labor costs while minimizing the volume of wasted products.

Our Approach

To address our client’s needs, we developed a state-of-the-art computer vision model that utilizes video intelligence techniques. The model efficiently processes videos of fruits and vegetables by employing noise and shadow reduction techniques such as Fourier Transformation, Normalization, Gaussian Blur, and more. Color variations and segmentation issues are tackled using gradient vectors and K means clustering techniques on pixels. Detection of local fruits and vegetables was configured using YOLO and Darknet.

We prepared a separate dataset of fruit and vegetable images with manually annotated quality ratings ranging from 1 (fresh) to 5 (decomposed). To predict the quality of items, we employed a pre-trained ResNet 50. Our solution was embedded in the client’s preferred physical device – NVIDIA Jetson Nano. The client used a 5MP camera with 10 ATP of recording under normal lighting for inspection purposes. Jetson Nano processed videos at 30 fps and produced an output of 10 fps.

Business Impact

Our automated solution has proven to be highly effective, resulting in a 25% reduction in product wastage within the first week of implementation. After four months of operation, the solution led to a further 35% reduction in manual labor costs.

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