The amount of food being produced, processed, packaged and shipped is huge. With the world population expected to reach almost 10 billion people by 2050 , it’s safe to say that there is going to be an increasing demand for food in the future.
Machine vision software has changed production dramatically since its conception in 1954 . Implementing machine learning and computer vision has had a huge impact on production rates and turnover. Throughout this article, we will be taking a look at the most important ways that machine vision software is improving production in food packaging.
The most obvious way in which machine learning and computer vision is being used is with automated visual inspection of products throughout the packing process. This method allows for real-time monitoring of the process to ensure that products are being packaged correctly and any defects can be detected early.
One example of this is a project that was run by a UK-based company called B&F . This project used machine vision software to check whether bread rolls were positioned correctly in packaging. The initial levels of automation reduced operator involvement from 100% to around 7%. The system has since been up-graded and is now fully automated.
Other ways that machine vision software is improving production in food packaging include:
Sorting different products based on quality, flavour etc. This ensures that the bulk of products are sent out correctly and any defective products can be detected and removed before being sent out to the general public.
Making sure that products are correctly stacked and aligned in pallets means that they can be more efficiently transported across longer distances which reduces costs and improves margins.
Measuring a range of different product features, such as weight, height etc., to ensure they meet the standard required for that specific product.
Many companies are now using machine vision software in their production facilities to improve production rates, reduce costs and allowing them to offer a higher quality of products. The ability to ensure that products meet certain standards is enabling food producers to increase margins while supplying high-quality food products to the general public.
The future of machine vision software looks bright, and it will be interesting to see how other industries will be able to take advantage of the benefits that it offers. With the ever-growing population and the demand for food that comes with it, it’s clear that machine vision software is going to play a huge role in ensuring that the food production process remains efficient and effective.
Machine vision software for industrial applications is also used in a wide variety of other industries, including medical manufacturing and pharmaceutical manufacturing.
Disadvantages of machine vision in food industry:
1. The high cost of implementation and maintenance of machine vision systems may limit its widespread adoption in the food industry.
2. Incorrect identification or classification of products can lead to costly product recalls.
3. Limited by the number of cameras that are installed, so large areas cannot be monitored simultaneously.
4. Sensors and lighting conditions vary between production lines, making it difficult to maintain a consistent level of image quality.
5. Machine vision systems rely on the installation of fixed-mount cameras which may not be suitable for certain types of factory layout or abnormal product shapes/sizes.
In conclusion, it is clear that machine vision software is improving production rates and offers many other advantages in food packaging. The high cost of implementation may limit its widespread adoption, however it will continue to be used in more complex systems where the risk of incorrect identification or classification of products can cause costly product recalls.