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Real-Time Egg Detection Using Edge Computer Vision

Authors:
Nicholas Hadjisavvas
Nicolas Nicolaou
Efstathios Stavrakis

Keywords: egg counting; smart retrofitting; deep learning.

Abstract:
The adoption of Artificial Intelligence (AI) in agriculture and animal husbandry has accelerated in recent years, driven by the versatility and relatively low costs for development and deployment of smart systems. However, many farms still rely on aging equipment and manual labour rendering these innovations inapplicable. In turn, the inability to harness AI and modernise operations may pose an existential risk. To address this challenge, we advocate for retrofitting existing machinery with AI-based modules as a practical alternative. In this paper, we demonstrate how a poultry egg grading machine can be enhanced with smart capabilities through the integration of deep learning and low-cost commodity edge hardware to enable precise egg counting. We present the methodology and algorithms behind this system that enables real-time processing while maintaining high accuracy. In a limited set of experiments, we demonstrated that the Raspberry Pi~5 (RPi5) running the EfficientDet-lite0 model performed just as well as a desktop with an NVIDIA GPU, accurately counting all the eggs it was presented with.

Pages: 16 to 22

Copyright: Copyright (c) IARIA, 2024

Publication date: November 17, 2024

Published in: conference

ISBN: 978-1-68558-324-8

Location: Valencia, Spain

Dates: from November 17, 2024 to November 21, 2024