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Real-Time Pig Weight Assessment and Carbon Footprint Monitoring Based on Computer Vision.

Min Chen1, Haopu Li2, Zhidong Zhang1

  • 1Department of Big Data and Intelligent Engineering, Shanxi Institute of Technology, Yangquan 045000, China.

Animals : an Open Access Journal From MDPI
|September 13, 2025
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Summary

Reducing the carbon footprint in pig farming is crucial for sustainability. This study used deep learning for real-time weight estimation to optimize feeding, significantly cutting feed, manure, and overall carbon emissions.

Keywords:
carbon footprintdeep learninglightweight modelprecision agricultureweight estimation

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Area of Science:

  • Agricultural Science
  • Environmental Science
  • Computer Science

Background:

  • Addressing the carbon footprint in pig production is essential for achieving carbon neutrality and peak emissions.
  • Systematic study of the carbon footprint is necessary for effective environmental goal realization.

Purpose of the Study:

  • To reduce the carbon footprint in pig production through optimized feeding strategies.
  • To minimize carbon emissions by employing data-driven insights.

Main Methods:

  • Conducted full-lifecycle carbon footprint monitoring from December 2024 to May 2025.
  • Developed and utilized EcoSegLite, a lightweight deep learning model for non-contact, real-time pig weight estimation.
  • Integrated EcoSegLite with a life cycle assessment (LCA) framework for feeding management optimization.

Main Results:

  • Achieved a 7.8% reduction in feed intake and an 11.9% reduction in manure output.
  • Reduced the overall carbon footprint by 5.1% through optimized feeding strategies.
  • Validated the effectiveness of the optimized strategy via growth curves and potential reductions in water consumption and nitrogen runoff.

Conclusions:

  • The study presents a data-driven solution for enhancing resource efficiency and reducing environmental impact in pig production.
  • Optimized feeding strategies, enabled by AI-driven weight estimation, offer a pathway to sustainable livestock farming.
  • The findings support precision agriculture and contribute to achieving carbon neutrality goals in the agricultural sector.