AI at the Edge

Chapter 12. Use Case: Food Quality Assurance

Industrial edge AI is used in food quality assurance to automatically detect and correct food defects and prevent food spoilage. This is done by training a machine learning model to recognize patterns in food images or from various industrial sensors that indicate a defect. The model is then deployed on an edge device, such as a camera, to automatically detect and correct defects in real time. This helps to ensure that food is of the highest quality and minimizes waste.

By using edge AI, food waste can be prevented by monitoring and managing food production and distribution more effectively. For example, if there is a problem with food spoilage, edge AI can be used to track the problem and take corrective action. In this chapter, we will brainstorm various approaches to using edge AI for food quality assurance purposes, their associated sensor and device configurations, and a deep dive tutorial into our selected approach and use case solution.

Problem Exploration

The term “food quality assurance” is too broad of a concept to tackle in this one chapter and too large of a problem to be solved with just one machine learning model; so for the purposes of this book, we will focus on food quality assurance in terms of preventing and minimizing food waste in a home kitchen environment, on a food product manufacturing line, or in cold storage/pantry shelving at a grocery store.

Preventing food waste can come in many different forms. Goal-wise ...