Efficient Food Manufacturing Enabled by Prism Cameras

James Cameron

© JAI

inspect: How has the food industry evolved with machine vision and what are the trends for the next years?

James Cameron: Over the past five years, machine vision in the food industry has evolved from a static inspection tool into a more intelligent system to secure quality-assurance and even predict data. In the fruit sector, deep learning and multispectral imaging are becoming more standard to detect outside surface defects together with internal defects, resulting in reliable grading, fewer false rejects, and the ability to estimate internal quality factors like sugar content or bruising non-destructively.

The shift to edge processing and embedded AI has allowed real-time decisions directly on the production line, improving consistency and reducing waste. Over the next years, the focus will shift increasingly toward predictive vision systems. Deep learning models will not only classify defects but forecast freshness, ripeness, and shelf life. We’ll also see growing use of 3D and laser-based imaging for precise localization and robotic handling for packaged goods.

Future growth will come from modular and customized vision platforms with optical lane sorters and free-fall sorters to adopt customer needs. These systems will connect inspection data across the entire value chain to packaging making machine vision one of the key enablers for efficient and waste-reduced food manufacturing. The optical sorter vision market is already large and growing double digits, signifying the critical role of vision adoption for the food industry.