Tracking E2 meat containers

How to automate meat recognition in an E2 container

Automatic recognition of the type of meat in an E2 container(click here to read more about E2 containers) is made possible through the use of vision technology and machine learning. The most common approach is to analyze images from a camera placed above the production belt that moves the meat containers.

An example algorithm for automatically recognizing the type of meat in an E2 container could work as follows:

1. Camera image acquisition – a camera placed above the production belt captures an image of an E2 container of meat.
2. Image segmentation – by using image processing algorithms, the E2 meat container is highlighted against the background.
3. Feature extraction – important features such as color, shape, texture of meat are extracted from the E2 container image.
4. Classification – based on the extracted characteristics, the algorithm classifies the type of meat contained in the container E2, e.g. beef, pork, poultry, individual classes, e.g. shoulder of class A, shoulder of class B, etc.

Machine vision and machine learning recognition technologies are increasingly being used in the meat industry, making it possible to automate production processes and minimize the risk of human error during meat classification. It is worth noting, however, that in order to achieve high algorithm performance, it is necessary to have a sufficiently diverse learning database to accurately recognize different types of meat.

Software innovations in the meat industry

Artificial intelligence (AI) and machine learning are widely used in meat production vision algorithms, as they allow automatic processing and analysis of vision data, which in turn enables faster and more precise meat processing.

Here are some examples of the use of artificial intelligence in vision algorithms for meat production:

1. Meat classification – as I mentioned earlier, AI and machine learning can be used to automatically recognize the type of meat inside an E2 container. This allows meat to be processed efficiently, minimizing the risk of human error during meat classification.
2. Meat damage detection – machine learning-based vision algorithms can be used to detect meat damage such as cuts, cracks, bruises, etc. This allows the elimination of damaged meat automatically and reliably.
3. Production process optimization – Artificial intelligence can be used to optimize production processes, such as meat cutting. Vision algorithms can automatically detect the shape, size and position of meat, which in turn allows for precise and fast processing.
4. Quality control – AI and machine learning can also be applied to meat quality control. Vision algorithms allow automatic assessment of meat quality, such as by analyzing color, texture, consistency, etc. This makes it possible to quickly detect meat quality and remove defective batches.

In summary, artificial intelligence and machine learning are increasingly being used in vision algorithms in meat production, making it possible to automate production processes and minimize the risk of human error during meat processing. Scanway uses both off-the-shelf models and application-specific proprietary analytical engines in its projects