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.
- Pub Date:
- November 2019
- Electrical Engineering and Systems Science - Signal Processing;
- Computer Science - Machine Learning
- Presented at NeurIPS 2019 Workshop on Machine Learning for the Developing World