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

This is the authors' abstract. We don't add key points for this paper.

Greenhouse 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 However, the methods for quantifying these emissions rely on manual and outdated data collection and processing approaches.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 (CH 4) and Nitrous Oxide (N 2 O) 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 (CO 2 eq) per year, with CH 4 at 282,282.96 kg CO 2 eq per year (88 %) and N 2 O at 38,838.38 kg CO 2 eq per year (12 %). Enteric fermentation contributed the highest emissions, about 99 % of the total CH 4 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.