Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

6.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
6.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Capture, storage, and re-use of liquid hydrogen boil-off for aviation and aerospace applications.

Nature communications·2026
Same author

Duration of super-emitting oil and gas methane sources.

Nature communications·2026
Same author

Seasonality and Declining Intensity of Methane Emissions from the Permian and Nearby US Oil and Gas Basins.

Environmental science & technology·2025
Same author

Upcycling Metal(loid) Contaminants to Produce Critical Raw Materials: The Nexus of Water Treatment and Material Criticality.

Environmental science & technology·2025
Same author

High-resolution national mapping of natural gas composition substantially updates methane leakage impacts.

Nature communications·2025
Same author

Imprint of Anthropogenic Sources and Soil Removal on the Surface Concentration of H<sub>2</sub> in the Contiguous US.

Environmental science & technology·2025

Related Experiment Video

Updated: Sep 28, 2025

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
11:02

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

22.3K

Artificial Intelligence Approach for Estimating Dairy Methane Emissions.

Seongeun Jeong1, Marc L Fischer1, Hanna Breunig1

  • 1Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States.

Environmental Science & Technology
|April 1, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) estimates California dairy methane emissions using aerial images. This AI method accurately predicts herd size, offering a cost-effective alternative for greenhouse gas (GHG) inventory development.

Keywords:
aerial imageartificial intelligencedairyemissiongreenhouse gasmethane

More Related Videos

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

16.9K
Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure
06:52

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

Published on: July 19, 2018

6.4K

Related Experiment Videos

Last Updated: Sep 28, 2025

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
11:02

The Use of an Automated System GreenFeed to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

22.3K
Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions
08:18

Design and Use of a Full Flow Sampling System FFS for the Quantification of Methane Emissions

Published on: June 12, 2016

16.9K
Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure
06:52

Lab-Scale Model to Evaluate Odor and Gas Concentrations Emitted by Deep Bedded Pack Manure

Published on: July 19, 2018

6.4K

Area of Science:

  • Environmental Science
  • Agricultural Science
  • Artificial Intelligence

Background:

  • California's dairy sector is a major source of anthropogenic methane (CH4) emissions, contributing approximately 50% to the state's greenhouse gas (GHG) inventory.
  • Existing atmospheric inverse modeling studies often rely on outdated geospatial data for dairy facilities, which can impact emission estimations.
  • The San Joaquin Valley (SJV) in California houses approximately 90% of the state's dairy population, making it a critical region for emission monitoring.

Purpose of the Study:

  • To develop and apply an artificial intelligence (AI) method for estimating dairy methane (CH4) emissions in California's San Joaquin Valley (SJV) using aerial imagery.
  • To accurately estimate facility-scale herd sizes and subsequently quantify enteric and manure CH4 emissions from the SJV dairy sector.
  • To assess the potential for CH4 emission reductions through the adoption of anaerobic digesters in large dairy facilities.

Main Methods:

  • Utilized artificial intelligence (AI) to process 316,882 aerial images for estimating facility-scale herd sizes across the SJV.
  • Validated the AI-driven herd size predictions against human visual inspection, achieving a correlation greater than 95%.
  • Applied the AI-estimated herd sizes to calculate enteric and manure CH4 emissions for the SJV dairy sector in 2018.

Main Results:

  • The AI approach demonstrated a high correlation (>95%) with human visual inspection for herd size estimation, offering a low-cost, efficient alternative.
  • Estimated SJV dairy enteric and manure CH4 emissions for 2018 to be between 496-763 Gg/yr (mean = 624 Gg/yr).
  • Identified 162 large dairy farms and estimated an 83 Gg CH4/yr reduction potential from anaerobic digester adoption.

Conclusions:

  • AI-powered analysis of aerial imagery provides a scalable and accurate method for estimating dairy herd sizes and associated methane emissions.
  • The developed AI approach can significantly improve the accuracy and cost-effectiveness of greenhouse gas (GHG) inventory development for the dairy sector.
  • The methodology holds potential for characterizing manure management systems and estimating GHG emissions in other agricultural sectors.