I just mean finding correlations in data sets that are hard to find in other ways. The main idea is that there are plenty of data sets on various cultivars and experiments for how to increase yields. There are probably patterns in the data that would be amenable to analysis by neural networks. The article gives an example for how scheduled flooding can increase yields and I bet there are a lot of low hanging fruits like that to pick. This doesn't require discovering anything novel but simply surfacing some patterns in the data that is buried across several papers and hard to uncover by classical meta-analysis and statistical techniques. Neural networks are very good for uncovering non-obvious statistical correlations which can then be verified by experimentation.

After reading the article I'm sure there are plenty of low hanging fruits to uncover in yield optimization by trying different schedules for flooding and soil enrichment with different kinds of fertilizers. A neural network doesn't have to understand anything to point out useful statistical correlations just like it doesn't have to understand code semantics for incomplete code fragments to suggest potential completions which are then verified by the programmer/compiler/type system.

I would speculate, but don't concretely know, that this is what will happen. I know papers in other fields that were just this; analyzing conditions that successful and failed experiments were performed in and then using ML to derive optimal conditions.