Environmental Monitoring & AI
Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence
This analysis leverages cutting-edge AI to derive actionable insights from the research titled "Quantification of greenhouse gas emissions from livestock using remote sensing & artificial intelligence". Discover how these advancements can be applied to revolutionize environmental monitoring, agricultural efficiency, and climate change mitigation strategies within your enterprise.
Executive Impact
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. It successfully trained a YOLO v4 deep learning algorithm to detect cattle from UAV images and then used the Simple Online Real-time Tracker (SORT) algorithm for automated counting. The study quantified methane (CH4) and nitrous oxide (N2O) emissions from enteric fermentation and manure management, revealing that enteric fermentation is the primary contributor to GHG emissions in grazing cattle. The method achieved high accuracy, demonstrating the potential for scalable and efficient monitoring of livestock GHG emissions for climate change mitigation.
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Overall F1 Score
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Average Precision
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Average Recall
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CH4 Contribution of Total GHG Emissions
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N2O Contribution of Total GHG Emissions
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
GHG Quantification Workflow
UAV Image Acquisition
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Data Preprocessing & Annotation
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Data Augmentation
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YOLOv4 Model Training
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Object Detection & Counting (SORT)
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GHG Emission Calculation (IPCC Tier 1)
Cattle Detection Accuracy
88.9%The YOLOv4 model achieved an average F1 score of 88.9% on the testing set, demonstrating high accuracy in cattle detection and counting from UAV imagery.
| Model | Accuracy (F1 Score) | Computational Cost | Key Advantages |
|---|---|---|---|
| YOLOv4 (This Study) | 88.9% | Low |
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| Faster R-CNN | High (e.g., 94%) | High |
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| Mask R-CNN | Very High (up to 94%) | Very High |
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| LCFCN/CSRNet | 57.1% - 67.6% | Moderate |
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Real-world Application: Kisombwa Ranching Scheme
Context: The Kisombwa Ranching Scheme in Mubende District, Central Uganda, served as the study area. This region is part of Uganda's cattle corridor, specializing in large-scale beef and milk production.
Challenge: Traditional methods for quantifying livestock GHG emissions rely on manual and outdated data, leading to inefficiencies and inaccuracies, especially in large-scale grazing systems.
Solution: Deployment of UAVs for high-resolution aerial imagery combined with a YOLOv4 deep learning model for automated cattle detection and counting, followed by IPCC Tier 1 guideline application for GHG quantification.
Results: A total of 170 cattle were counted, leading to an estimated 321,121.34 kg CO2eq per year. Enteric fermentation contributed 87% of total GHGs, highlighting the method's effectiveness in providing actionable data for mitigation strategies.
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