Digital- Twin Enabled Dairy Farming for Greenhouse Gas Emission Tracking

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

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.

Citation

Ak, E., Huseynov, K., Canberk, B., Fahim, M., Dobre, O. A., & Duong, T. Q. (2023, December). Digital- Twin Enabled Dairy Farming for Greenhouse Gas Emission Tracking. Presented at 2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS), Letterkenny, Ireland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)
• Start Date: Dec 7, 2023
• End Date: Dec 8, 2023
• Online Publication Date: Mar 20, 2024
• Publication Date: 2024
• Deposit Date: Sep 3, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Book Title: 2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)
• DOI: https://doi.org/10.1109/aics60730.2023.10470605
• Public URL: http://researchrepository.napier.ac.uk/Output/3633033