Quantized deep learning models on low-power edge devices for robotic systems

TL;DR

This paper demonstrates the deployment of quantized deep neural networks on low-power edge devices to control robotic systems for agricultural tasks, emphasizing sustainability, privacy, and autonomy.

Contribution

It introduces a novel application of quantized deep learning models on edge devices for agricultural robotics, highlighting practical deployment in real-world farming environments.

Findings

Successful deployment of quantized models on low-power hardware

Robotic control for agricultural tasks achieved with edge AI

Potential for increased sustainability and privacy in farming

Abstract

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.