Machine Vision has been in the food industry for quite a long time. However, its latest developments are supporting a broad array of exiting industrial technologies.
FREMONT, CA: With the machines becoming more intelligent every day, the food industry is experiencing abundant modern applications and numerous ways to get benefitted from it. For the industry, machine learning and machine vision are explicitly valuable additions to the supply chain.
Machine vision’s application in the food industry
Machine vision can be applied in ample ways to a food processing environment, with the latest variations on the technology cropping up frequently. The following is a rundown on the ways distinct types of machine vision systems serve different functions in the food and beverage domain.
1. Automated Sorting for Big Product Batches
It is very easy for machine vision inspection systems to become part of a colossal automation effort. Automation is of great benefit to the food and beverage sector in terms of improving worker safety and proficiency. Besides, it also offers better quality control across the organization.
Inspection stations having machine vision cameras scan every single product or whole batches of products for detecting flaws. However, physically segregating these items might be just as efficient a process as detecting them. Due to this reason, machine vision is a suitable companion to compressed air systems and others, which can blow away and eliminate every single grain of rice from a bigger batch in preparation.
2. 3-D Machine and Frame Grabbing
Machine vision systems need optimum lighting for carrying out successful inspections. Unwanted products can slip through easily onto shelves and into clients’ homes if the part of scanning is in shadow.
When it comes to carrying out visual inspections, sometimes food products have explicit requirements. It is hard or sometimes impossible for the human eyes to perform detailed scanning of thousands of nuts or peas as they pass over a conveyor belt. The 3-D machine provides a tool known as ‘frame grabbing,’ which takes stills of—possibly—tens of thousands of small, moving items at once in order to find errors and perform sorting.
3. Near-Infrared Cameras
Machine vision comes in many forms, which include barcode and QR code readers. The latest technology, known as near-infrared (NIR) cameras, is already significantly enhancing the usefulness and capabilities of machine vision.
In many cases, physical damage to vegetables and fruits does not immediately surface on the outside. NIR technology does the job of expanding the light spectrum cameras can observe, thus providing them with the capability to identify interior damage before it appears on the exterior.
It is vital to understand that neither machine vision nor machine learning is about developing hardware that that thinks and observes as humans do. With the appropriate approach, these systems can outperform human employees.