Cameras, sensors, and software combine to capture, analyse, and interpret visual data, performing pre-determined tasks like picking, barcode scanning, quality inspection, measuring, sorting, and much more.
Sectors such as automotive, logistics, e-commerce, and food and beverage are developing new machine vision applications seemingly by the day. Machine vision can spot when a box is not closed properly; it can communicate whether something is broken; it can even analyse size and identify defects to determine a ‘good’ potato from a ‘bad’ one.
But what are the practical steps companies need to take to adopt machine vision effectively?
Read the full article in DPA's March 2026 issue