Machine-learning identifies tiger and leopard species with 79% accuracy from waste samples
GS3Economy · S&T · Environment · Security· Species, protected areas & wetlands· Prelims·
Why in news
Researchers published a study in the Journal of Chemical Ecology using machine-learning to identify tiger and leopard species from waste samples.
Background
A team from the National Centre for Biological Sciences, Ahmedabad University, and the Danish Academy of Technical Sciences achieved 79% accuracy for urine and 75% for scat samples. The study analyzed volatile organic compounds to identify species and physiological traits.
Facts for Prelims
- S&TMachine-learning was used to analyze volatile organic compounds in tiger and leopard waste.
- FactThe study achieved 79% accuracy for urine samples and 75% for scat samples.
- BodyThe research involved the National Centre for Biological Sciences, Ahmedabad University, and the Danish Academy of Technical Sciences.
For Mains
Q. Discuss how machine-learning and chemical analysis can enhance wildlife conservation and monitoring of endangered species in India.
Dimensions to cover in your answer
- Data-driven conservation: Using volatile organic compounds for non-invasive monitoring of elusive predators
- Technological integration: Leveraging AI to overcome limitations of traditional physical tracking in dense habitats
Keywords: Machine Learning · Volatile Organic Compounds · Wildlife Conservation · Non-invasive Monitoring · Biodiversity Tech
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