Advancing EarthSense AI-driven Livestock Farm Management for Climate-Resilient Food Security

Project Member(s): Abdollahi, A.

Funding or Partner Organisation: Worldwide Universities Network (WUN Research Development Fund)
Worldwide Universities Network (WUN Research Development Fund)

Start year: 2025

Summary: The EarthSense AI project aims to transform livestock farm management by integrating advanced earth observation technologies with responsible AI. Leveraging existing datasets from ecological field measurements with practical farm experience, alongside satellite imagery including optical and RADAR sensors and collecting multispectral/hyperspectral drone surveys, the project will develop a fully benchmarked AI-driven sensing prediction model. This AI-driven sensing technology will provide farmers with critical information on pasture quality, utilization, and sustainability metrics (e.g., grazing land health, condition, soil and biodiversity), empowering them to optimize land management and long-term productivity. The project will focus on pilot regions in Australia to demonstrate proof-of-concept, conducting thorough testing and validation under various scenarios and climate conditions. Emphasizing ethical and transparent AI ensures farmers can make informed decisions that enhance food security and agricultural sustainability. The project implements explainable models and visualization tools to provide clear insights into decision-making processes, building trust among farmers and allowing them to understand and validate AI recommendations. Through AI-driven sensing, predictive analytics, and scenario planning, EarthSense AI will offer valuable insights into climate change impacts on Australia's unique pasture and livestock ecosystems. This innovative approach aims to increase carrying capacity, optimize livestock production, and improve profitability while maintaining ecological balance. By developing a robust model that can be generalized to other regions/countries, EarthSense AI will not only enhance Australia's agricultural sector but also contribute significantly to global advancements in sustainable food production technologies.

FOR Codes: Earth and space science informatics, Agricultural spatial analysis and modelling, Sustainable Cities and Communities, Assessment and management of terrestrial ecosystems, Expanding knowledge in the agricultural, food and veterinary sciences

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.