Abstract:
A major problem that a farmer faces, while adopting a new crop
variety in the farm; is the uncertainty associated with its growth.
Farmers working on real farms are not aware of the growth models, even for the existing crops. Hence, there is a need for more
accessible and intuitive models. This work is a step towards the
realization of another promising model, which is the digital twin
of a crop. A primary requirement of the digital twin is the digital
representation of the crop itself. Extending that notion, the work
discusses the development of 3D assets of crops and their temporal alignment. It also describes the methodology involved in the
development of a VR framework, which stores the ideal growth of
a crop. This framework could be useful to farmers who want to
confirm the growth of their crops. Furthermore, it also proposes a
quantitative metric to evaluate the VR framework. The consistency
of this proposed metric is further backed by a user study which is
based on a qualitative method.
