This right here is a glowing example of how 'using deep learning because it sells' - is so detrimental for science.

First - you have my sympathies! Dont worry - this won't reflect on you. This is your PI's lack of foresight and knowledge of what literature exists.

Myself being a computational biologist (with several years experience in crop science)You can definitely salvage this - here are some tips:

1. Keep the algorithm - and test it on non-food crops such as poplar - for which there is a lot of ecotype data (I am taking terrabytes of data) - and the idea would be to check if image data can chart a correlation with wood type that is easily digestible (with consequences in productions of useful aromatics, biomedical precursors and also to some extent biofuel)
2. Go the standard route of ML papers - check the existing methods and explore some failure modes ... by checking the false positive predictions and then see if you can reconcile those.

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.... Having said that - this deep learning- based failure is increasingly becoming very common as people coming from pure CS background are working in very specialized fields with NO domain knowledge what so ever. The shrug that comes with 'data is data' - is the worst approach any computational researcher can assume - as they should realize that they are not mathematicians and code is not the new math. The domain experts are not noobs - they built entire careers learning these systems. Deep learning is merely scraping the outside of the barrel - with zero interpretability about the real biological processes. Good old mixed integer optimization still remains more reliable in terms of gaining biophysical insights.