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Machine learning-assisted self-powered ear tag for animal welfare
Xiaoyu Su1,2, Peidi Fan1, Ying Liu1
1Laboratory of Agricultural Information Intelligent Sensing, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, PR China.
Nature Communications
|June 2, 2026
Summary
A new self-powered ear tag uses machine learning to continuously monitor animal metabolic health and ion balance in livestock. This technology accurately identifies welfare states, improving precision animal farming.
Area of Science:
- Animal Science
- Biomedical Engineering
- Sensor Technology
Background:
- Metabolic health is vital for animal welfare, but intensive farming practices often lead to nutritional imbalances and metabolic dysregulation.
- Identifying metabolically abnormal animals in large-scale production settings is difficult.
- Continuous, real-time monitoring is needed for effective livestock health management.
Purpose of the Study:
- To develop a machine learning-assisted, self-powered ear tag for continuous monitoring of ion homeostasis in livestock.
- To enable large-scale, energy-autonomous deployment for precision livestock health and welfare management.
Main Methods:
- A hybrid energy harvesting module (triboelectric nanogenerator and solar cell) powers the ear tag for autonomous operation.
- A microneedle-based biosensing module performs minimally invasive, time-resolved monitoring of interstitial fluid pH, K+, and Ca2+.
- A machine learning pipeline decodes multi-ionic marker dynamics to classify animal welfare states.
Main Results:
- The self-powered ear tag achieved over 95% average classification accuracy in distinguishing five distinct welfare-related states.
- Validation using leave-one-animal-out cross-validation across 5399 sampling windows from three independent animals confirmed robust performance.
- The system demonstrated energy-autonomous operation through an optimized duty-cycled strategy.
Conclusions:
- The developed self-powered ear tag offers a scalable technological solution for next-generation precision livestock health monitoring.
- This technology bridges energy autonomy with intelligent metabolic profiling, enhancing animal welfare management in intensive farming.
- Continuous, multi-ionic monitoring provides a powerful tool for early detection of metabolic dysregulation and welfare issues.
