Improving food quality
With the help of powerful machine vision technology
The world’ s population is growing steadily. In order to optimize the nutrition of billions of people, increasing automation of processes is required in many areas of the food industry. Powerful machine vision systems play a crucial role in optical quality control in this environment. In this interview James Cameron, Sales Manager EMEA at JAI, explains requirements and solutions for the food industry.
How has the food industry evolved with machine vision and what are the trends for the next five 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 qualityassurance 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 five 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.
10 FDPP- www. fdpp. co. uk
The optical sorter vision market is already large and growing double digits, signifying the critical role of vision adoption for the food industry.
What are the special demands for machine vision in the food industry?
James Cameron: In the optical sorting industry, fresh fruit is typically inspected while transported through cups or lanes, allowing each product to be stabilized for precise surface and color analysis. Whereas vegetables, grains, and rice are more often evaluated in free-fall sorters, where objects are analyzed mid-air for shape, color, and foreign material detection. These 360 degree inspections can only be achieved by free fall systems where the objects are checked while they fall. This obviously also involves high speeds during inspection.
In the optical sorting industry, fresh fruit is typically inspected while transported through cups or lanes, allowing each product to be stabilized for precise surface and color analysis.
To make things even more demanding, many of these applications need very accurate color data, to distinguish small and challenging defects that appear in the same colour range like finding dark yellow spots on French fries.
Another trend is the clearly growing demand for external and internal quality grading of food objects, which requires a multispectral or hyperspectral approach. This brings the need to look at both visible light for color inspection and NIR or SWIR wavelengths at the same time objects are passed. To check for damaged items that are not visible from the outside, for example bruises on apples that will sooner or later develop into rotten spots. An additional challenge for machine vision systems in these use cases in food sorting and grading is that rotating and spinning objects as well as speed variations can give blurry images.
Optical sorting of vegetables, grains, rice and more in the food industry is often evaluated in free-fall sorters, where objects are analyzed mid-air for shape, color, and foreign material detection.