  We describe the lessons learned from targeting agricultural detection
problem-solving, when subject to low resolution input maps, by means of Machine
Learning-based super-resolution approaches. The underlying domain is the
so-called agro-detection class of problems, and the specific objective is to
learn a complementary ensemble of sporadic input maps. While super-resolution
algorithms are branded with the capacity to enhance various attractive features
in generic photography, we argue that they must meet certain requirements, and
more importantly, that their outcome does not necessarily guarantee an
improvement in engineering detection problem-solving (unlike so-called
aesthetics/artistic super-resolution in ImageNet-like datasets). By presenting
specific data-driven case studies, we outline a set of limitations and
recommendations for deploying super-resolution algorithms for agro-detection
problems. Another conclusion states that super-resolution algorithms can be
used for learning missing spectral channels, and that their usage may result in
some desired side-effects such as channels' synchronization.
