September 7, 2026
For generations, India’s forest guards protected tigers with little more than their knowledge of the jungle, hours of foot patrols and the clues animals left behind. A pugmark in soft earth, scat beside a trail or an alarm call from a sambar could reveal that a tiger had recently passed.
Those traditional skills remain indispensable. But today they are being reinforced by GPS-enabled patrols, camera traps, mobile applications, artificial intelligence and even systems that listen to the sounds of the forest.
The result is a fundamental change in tiger conservation: instead of merely discovering where a tiger was, technology is increasingly helping forest teams understand where a tiger is—and what is happening around it—in near real time.
When pugmarks told the story
Before digital technology became widely available, tiger monitoring depended heavily on fieldcraft.
Forest personnel walked long distances searching for pugmarks, tracks, scat and other signs. India’s earlier tiger censuses attempted to identify individual animals by measuring and comparing pugmarks.
But footprints can vary according to soil, weather and the way an animal walks, while interpretation also depends on the observer. This made pugmark-based population estimates vulnerable to inconsistencies. Patrol observations, meanwhile, were generally entered manually into paper registers, meaning valuable information could take time to reach managers.
The experience of forest guards remained invaluable, but there was no easy way to rapidly combine thousands of individual observations into a larger picture of what was happening across a tiger reserve.
M-STrIPES puts patrols on the digital map
A major transformation came with M-STrIPES — Monitoring System for Tigers: Intensive Protection and Ecological Status.
The system introduced GPS, mobile applications and digital databases into tiger-reserve management.
Forest guards can record patrol routes, wildlife observations, crime scenes and other information as geotagged data. Managers can consequently see which areas are being adequately patrolled and identify locations where protection efforts may need strengthening.
Instead of information remaining confined to individual notebooks and registers, patrol activity can become part of a larger digital picture of the reserve.
Camera traps revolutionise tiger counting
Camera traps brought another dramatic advance.
Motion-triggered cameras positioned along forest trails photograph wildlife without requiring observers to be physically present. For tiger conservation, their greatest advantage comes from something every tiger carries naturally—its unique stripe pattern.
Researchers can use those patterns to distinguish one tiger from another, making population estimation considerably more robust.
The scale at which India now uses the technology is extraordinary. The country’s 2018 tiger assessment deployed cameras at 26,838 locations and produced more than 34 million wildlife photographs.
Camera traps therefore transformed tiger monitoring from following footprints to building photographic records of individual animals across enormous landscapes.
But conventional camera traps have an important limitation: researchers often learn what happened only after retrieving and processing the images.
That is where AI is beginning to change the equation.
AI cameras can raise the alarm within seconds
The newest generation of monitoring systems can analyse information where it is collected.
Edge-AI cameras such as TrailGuard can process images locally, recognise wildlife, people or vehicles and transmit selected alerts rather than waiting for researchers to manually examine thousands of photographs.
Trials in tiger landscapes have demonstrated just how significant that difference can be: alerts generated after detecting tiger images reportedly reached smartphones in around 30 seconds.
This opens possibilities extending well beyond tiger censuses.
A tiger approaching a village, an unauthorised person entering a sensitive forest area or suspicious vehicle movement could potentially be flagged while the event is still unfolding.
For anti-poaching operations and human-tiger conflict mitigation, those saved minutes can be extremely valuable.
Now technology is listening to the forest
Perhaps one of the most intriguing developments is taking place in Pench, where an AI-driven system is being tested to detect what animals themselves are saying.
Prey species such as deer produce distinctive alarm calls when they detect predators.
Bioacoustic monitoring can analyse these sounds and look for patterns suggesting the presence of a tiger or another large carnivore. Once detected, alerts can potentially be transmitted to forest personnel and nearby communities.
This could provide residents with advance warning when a big cat is approaching human-used areas and give wildlife teams additional time to intervene before an encounter escalates.
Nagarahole brings AI, GSM cameras and monitoring together
Technology is also being integrated into protection and conflict-management systems elsewhere.
The report points to Garuda at Nagarahole, documented by the National Tiger Conservation Authority, which combines technologies including AI, GSM-connected camera traps and real-time monitoring.
Rather than relying upon one device, such approaches attempt to create networks capable of detecting events, communicating information and enabling faster responses.
The real revolution is speed
The biggest change brought by technology may therefore not be the camera, smartphone, GPS receiver or artificial intelligence individually.
It is time.
A pugmark might tell a forest guard that a tiger had already walked through an area. A camera trap could establish which tiger had passed. GPS could reveal where protection teams had patrolled.
AI-enabled systems can potentially recognise an animal or threat and communicate that information while the event is still occurring.
That shift—from retrospective evidence to near-real-time intelligence—could have major implications for protecting tigers, combating wildlife crime and reducing encounters between people and large carnivores.
Technology cannot replace the forest guard
There is, however, an important lesson amid the technological advances.
Tiger conservation cannot simply be handed over to algorithms and cameras.
A device may recognise a tiger, but experienced field personnel understand its behaviour, landscape and local communities. Technology becomes most effective when it extends the capabilities of frontline forest staff rather than replacing their traditional knowledge.
India’s tiger-conservation story is therefore becoming a partnership between old and new.
The pugmark has not become irrelevant. Neither have the forest guard’s eyes and ears. Instead, footprints, fieldcraft and decades of experience are increasingly being combined with GPS coordinates, millions of photographs, connected cameras and artificial intelligence.
A century ago, a footprint in the mud might have been the only indication that a tiger had passed.
Today, the forest itself is beginning to send an alert.
Source credit: Based on The Better India report, “Once It Was Pugmarks. Now It’s AI: How Technology Is Helping Protect India’s Tigers,” by Nishtha Kawrani, edited by Vidya Gowri Venkatesh, published September 1, 2026.
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