  In this work, we present a quantized deep neural network deployed on a
low-power edge device, inferring learned motor-movements of a suspended robot
in a defined space. This serves as the fundamental building block for the
original setup, a robotic system for farms or greenhouses aimed at a wide range
of agricultural tasks. Deep learning on edge devices and its implications could
have a substantial impact on farming systems in the developing world, leading
not only to sustainable food production and income, but also increased data
privacy and autonomy.
