Can i use deep learning in my agriculture PHD?
Please note that the following is my personal opinion; I still hope that you find it useful.

If you cannot properly explain a concept on paper, then it should not be part of your PhD thesis. It is possible to implement Deep Learning algorithms without any conceptual knowledge. While this may be (somewhat) acceptable in the industry, it will get you into trouble in academia. 

I have no doubt that your algorithms are well-programmed and solve many issues that are challenging from a programmer's perspective. But that is not what the reviewers of your thesis are interested in. They are also not interested in the prediction accuracy of your algorithm. 
As scientists, they want to know why something happens; and to my knowledge, Deep Learning algorithms provide next to nothing on this question. 

And this is where 'middle age' linear regression has its strengths (by the way: a quick way to make an enemy is to tell him that the method he has been using all his life is too old to be useful). Yes, linear regression is unexciting, and it has many shortcomings. But it is extremely well understood. Most importantly, there are tons of books and papers about the connection between assumptions and valid interpretations: we know under which assumptions and in which sense the linear regression estimator is optimal; we know which assumptions lead to which kind of confidence intervals for the estimator; we know about robustness with respect to model misspecification; etc.

As long as you cannot address these issues convincingly and on paper (That is: In what sense is my result optimal? How confident can I be about my result? How robust is my model?), you should not use Deep Learning in research as an alternative to classical statistical methods.