Digital- Twin Enabled Dairy Farming for Greenhouse Gas Emission Tracking

Elif Ak, Khayal Huseynov, Berk Canberk, Muhammad Fahim, Octavia A. Dobre, Trung Q. Duong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The dairy farming industry plays a pivotal role in the agricultural sector. However, its environmental footprint, especially methane and nitrous oxide emissions, has raised concerns. Historically, the industry has relied on conventional methods to forecast and manage waste production and its subsequent carbon emissions. These methods, while functional, often fall short in terms of net-zero planning for dairy farming where instant and continuous monitoring is required. To address this gap, this study presents a novel framework that combines the capabilities of Digital Twin (DT) technology with the power of Machine Learning (ML). The primary objective of this framework is to pave the way for dairy farming practices that are sustainable and align with net-zero emission targets. The results show that when multi-context datasets are used, carbon emission can be predicted with high accuracy.

Original languageEnglish
Title of host publication2023 31st Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350360219
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event31st Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2023 - Letterkenny, Ireland
Duration: 7 Dec 20238 Dec 2023

Publication series

Name2023 31st Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2023

Conference

Conference31st Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2023
Country/TerritoryIreland
CityLetterkenny
Period7/12/238/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

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