Yes thats true. This is also what I thought about. Using a mixed dataset or transfer learning approaches (first train on synthetic data, then retrain on real world) should incorporate the domain knowledge. But you are right, right know thats just an hypothesis...but I will test it!

Hi, interesting! Do expert models exist for your problem already or would it be only the knowledge database you want to combine?

Thanks a lot! That looks like a very interesting approach! I will have a detailed look into it!

Thanks for that suggestion! Yeah I had thoughts about this. The problem is that plant crop probably has not so binary solutions like a engine status... Maybe a very simple "rule" (e.g. a functions of water access and crop yield) could be added into the loss function. If this easy expert knowledge output a high probability that the plant died (and yield=0) all y_train could be set to 0 also.... However, crop growth relies on so many events that happens during growth, that it would mean to implement many many rules...

I would say both. I have an actual problem (to predict crop yield as accurate as possible) but the way there is definitely a research problem... What proven methods would you think about?

Thanks, yes that is true, the recent days I had a look into Bayesian Statistics. That might be an alternative to pure ML that I am considering right now