Today’s good news story comes from the India Times and was submitted by our overseas reporter, Noelle Granger. Thanks Noelle
Meet Anika Puri, the 17-year-old Indian-American student who built a $300 AI system that spots elephant poachers with over 90% accuracy and won a $25,000 Davidson scholarship.
TOI Science Desk / TIMESOFINDIA.COM / October 13, 2026, 20:00 IST
When darkness settles over a wildlife reserve, spotting a poacher from hundreds of feet up can be difficult, even with thermal imaging technology. For park rangers protecting endangered animals, a missed movement could have serious consequences. Anika Puri, an Indian-American student from Chappaqua, New York, set out to tackle this challenge with an unlikely combination of artificial intelligence, drone technology, and a closer look at how humans and animals move. According to a Society for Science report, her project, ElSa (short for Elephant-Savior), developed a low-cost system to identify potential poachers in nighttime drone footage. The innovation earned Puri a $25,000 scholarship as a 2022 Davidson Fellow, recognizing her engineering work and its potential application to wildlife conservation. Scroll down to read more about her journey. Turning curiosity into innovation. According to a Davidson Institute online release, Puri’s interest in technology grew from an early fascination with building, experimenting, and exploring new ideas.
From presenting a Lego Mindstorms robot at Maker Faire to developing an interest in drones and earning a commercial drone pilot license, she approached engineering as a way to turn curiosity into practical solutions. Her focus eventually shifted to environmental science and protecting endangered species. During visits to India, she was struck by the continued presence of ivory jewelry and statues in Mumbai markets, despite international efforts to curb the illegal ivory trade. The experience prompted her to investigate the scale of wildlife poaching and the technology being used to prevent it.
She discovered that drones equipped with thermal infrared cameras were already helping rangers monitor protected areas after dark. However, analyzing the resulting footage remained a challenge, the release explains.
Why traditional detection falls short
Drones monitoring wildlife reserves typically fly at considerable heights to cover large areas while working within limited battery life. At around 400 feet, people and animals can appear as small shapes in thermal images, making them difficult to distinguish accurately, the online release states. Existing detection systems often rely on analyzing individual frames and identifying objects by their shape. According to Puri’s research, these approaches could achieve accuracy as low as 20% in challenging conditions. Human operators watching hours of footage also risk fatigue and missing activity.
Puri approached the problem from a different angle. Instead of examining only what appeared in an image, she studied how objects moved over time. Reading movement through AI. The ElSa system uses machine learning to analyze spatio-temporal patterns in nighttime thermal infrared videos. These patterns include differences in speed, turning radius, and movement behavior, as well as whether an animal moves as part of a herd, adds the Society for Science report. By examining these characteristics across successive video frames, the algorithm aims to distinguish humans from animals more reliably than methods based primarily on static images.
When tested on the BIRDSAI thermal infrared video dataset, collected from four national parks in Africa, the system achieved 90.9% accuracy in detecting humans. The result represented roughly a fourfold improvement over the existing methods cited in her research. Although identifying a human does not automatically establish that the person is a poacher, the technology could help rangers flag suspicious activity for further investigation. Making conservation technology affordable.
Cost was another central consideration in Puri’s design. High-resolution thermal cameras can cost about $4,800, putting advanced surveillance technology out of reach for some conservation programs. Her ElSa prototype, built using commercially available components, cost around $300. This significantly lower price point could make automated detection tools more accessible to parks in Africa and Asia, where budgets and resources are often limited.
The project offers a glimpse of how artificial intelligence can be applied beyond conventional commercial uses, addressing challenges that affect biodiversity and environmental sustainability.
A young researcher with a larger purpose
For Puri, the Davidson Fellowship recognized a project that connected engineering to a cause she cared deeply about. Her work demonstrates how looking at a familiar problem differently can open up new technological possibilities. Wildlife protection requires trained rangers, effective enforcement, community participation, and reliable monitoring. ElSa is not a substitute for those efforts. Still, it points towards a future in which affordable AI tools could help conservation teams identify threats faster and direct their limited resources more effectively. Images Courtesy: Society for Science
The good news here is a young person took the initiative to improve existing technology. Today’s JohnKu talks about Initiative. I hope you have a super weekend.
Go by John W. Howell © 2026
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