Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence

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dc.contributor.authorNaturinda, Evet;
dc.contributor.authorKemigyisha, Fortunate;
dc.contributor.authorGidudu, Anthony ;
dc.contributor.authorKabenge, Isa;
dc.contributor.authorOmia, Emmanuel;
dc.contributor.authorAboth, Jackline
dc.date.accessioned2025-08-25T12:24:13Z
dc.date.available2025-08-25T12:24:13Z
dc.date.issued2025-07-18
dc.description.abstractGreenhouse gases (GHGs) from agriculture in Africa are among the world's fastest-growing emissions, with the livestock sector as the primary contributor. However, the methods for quantifying these emissions rely on manual and outdated data collection and processing approaches. Therefore, there is a need to develop more accurate and efficient methods of quantifying GHGs from livestock. This research developed a remote sensing and Artificial Intelligence (AI) based approach to quantify GHG emissions from cattle in the Kisombwa Ranching Scheme in Mubende District, central Uganda. We trained a deep learning algorithm, You Only Look Once (YOLO) v4, to detect cattle from the Unmanned Aerial Vehicle (UAV) images of the study area and applied the Simple Online Real-time Tracker (SORT) algorithm for automated counting. Methane (CH4) and Nitrous Oxide (N2O) emissions from manure management and enteric fermentation were estimated using the number of cattle and the Tier 1 guidelines from the Intergovernmental Panel on Climate Change (IPCC). The total estimated emissions were 321,121.34 kg carbon dioxide equivalent (CO2eq) per year, with CH4 at 282,282.96 kg CO2eq per year (88 %) and N2O at 38,838.38 kg CO2eq per year (12 %). Enteric fermentation contributed the highest emissions, about 99 % of the total CH4 emissions and 87 % of the total GHGs. The proposed remote sensing and AI-driven method achieved an average F1 score of 88.9 %, average precision of 97 %, and average recall of 82.9 % on the testing set of images. Therefore, these research findings demonstrate that remote sensing and AI are a more potent and efficient approach to upscale quantifying and reporting animal population and livestock GHG emissions for sustainable agriculture and climate change mitigation. •Collected, preprocessed, and annotated Unmanned Aerial Vehicle (UAV) images.•YOLO V4 and SORT were used for cattle detection and counting from UAV images, and IPCC guidelines to estimate GHG emissions.•Achieved 88.9 % accuracy in counting grazing cattle and observed that CH4 dominated GHG emissions, contributing 88 % from enteric fermentation.
dc.identifier.citationNaturinda, Evet, Fortunate Kemigyisha, Anthony Gidudu, et al. 'Quantification of Greenhouse Gas Emissions from Livestock using Remote Sensing & Artificial Intelligence', Artificial Intelligence in Geosciences, vol. 6/no. 2, (2025), pp. 100147.
dc.identifier.issnISSN 2666-5441
dc.identifier.issnEISSN 2666-5441
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/12019
dc.language.isoen
dc.publisherElsevier B.V
dc.titleQuantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence
dc.typeArticle
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