V-FARM: Virtualized Farming Analytics for Smart Decision-Making

Haitham Mahmoud, Matt Bell, Neave Anderson, Elizabeth Cutter, Claire Edwards, Adel Aneiba, Umar Daraz, De Mi

Research output: Contribution to conferencePaperpeer-review

Abstract

The welfare, productivity, and health of livestock are being impacted steadily by climate change and extreme weather events. For instance, it is predicted that heat stress will cause dairy cows in Southeast England to lose more than 170 kg of milk a year; in the hottest UK regions, this loss could increase to 1,300 kg per cow (18.6% of annual yield) by the 2090s. A solid foundation for evaluating environmental stress is presented in this paper. It is based on agro-environmental indices that are obtained from temperature and humidity data. To improve predictive capabilities, the proposed Virtualized Farming Analytics for Smart Decision-Making (V-Farm) system uses state-of-the-art synthetic data generation techniques, such as TCGAN and polynomial fitting. A novel approach to lessen the effects of thermal stress in precision livestock farming is to incorporate these indices into automated ventilation systems.
Original languageEnglish
Pages186-191
Number of pages6
DOIs
Publication statusPublished - 9 May 2025
Event2025 IEEE 11th Conference on Big Data Security on Cloud -
Duration: 9 May 202511 May 2025

Conference

Conference2025 IEEE 11th Conference on Big Data Security on Cloud
Period9/5/2511/5/25

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