My background: I am a tech architect/manager on IoT projects who designs and helps develop analytics systems. I do some basic data modeling in my role, but defer to experts to build a better model once we find some area of interest.
The actual skills of light programming (R, Python), data literacy/manipulation and some basic modeling (statistical/machine learning, Bayesian methods, time series, etc) are useful for many job roles, and I think will be considered basic skills for college graduates in the near future. This isn't new - operations research, particularly Six Sigma and quality control, have used statistics and some light programming to solve business problems for decades.
By itself, I don't see Data Science evolving the way promised by schools and boot camps. Most of the positions named "Data Scientist" (at non-tech companies) are really just senior business analysts; I work with a group of them at my company and 90% of their day to day is just extracting and analyzing various reports for other managers and directors. When an interesting (and potentially lucrative) business problem does come along, they usually outsource to a specialized analytics firm and the data scientist helps coordinate that project. (If you have a good data dictionary, a clear outcome in mind, and some basic knowledge of the field it's relatively easy to outsource the advanced work.)
st1x7 had the best advice below -- learn the basic methods and then apply them to your field. If you google "agriculture iot research papers" you'll find tons of examples of people using sensors for data collection and then analyzing the data to improve some process.
TL;DR I see Data Science melting into other roles, but the basic skills/data literacy are useful for almost everyone.