Can YOLOv8 be used in real-time applications for continuous monitoring of meat freshness in market?
The best way to answer this question is to test it. If you have a particular use case in mind, there are lots of variables that will be specific to your particular situation which may cause it to work or not. It will be difficult for anyone to tell you with certainty that it will or will not work.
There are some questions that you can try to answer for yourself to determine if it’s a good idea to spend the time to do the test:
- Can whatever you’re attempting to detect, be easily seen visually?
- Are the categories/objects you’d like to detect visually distinct?
- How many different imaging conditions are there likely to be (different cameras, various lighting, movable cameras, variety of locations, etc.)?
- Can you combine the information collected from the detection data with other data to help accomplish your end goal?
Again, no one can tell you with certainty that YOLO will allow you to accomplish something in particular. It will always need to be something you test for yourself.
Particular input on your question. I doubt you’ll be able to detect “freshness” as this is an abstract idea. It’s more likely that if there are visible indicators that something is no longer considered “fresh” these could be detected. Definitely put some thought into what the true goal is for your project, then try to identify the key indicators that could be used/measured to accomplish that goal. If there are any that are visual in nature, then YOLO might be an option, but it’s still worth testing.