Leveraging artificial intelligence in the fight against global wildlife poaching

In April 2018, Kenya announced that Sudan, its last male northern Rhino, died at the age of 45, of age related complications. He’s survived by his daughter and grand-daughter but with no males left, the species’ proximity to extinction is perilously near. According to the World Economic Forum, Wildlife trafficking is the fourth most lucrative business in the world after drugs, humans and arms. The global illegal wildlife trade generates anything from US$7 billion to US$23 billion every year, and trophy hunting is estimated at US$200 million in annual revenue. Unfortunately, India doesn’t have much to gloat about in this domain. In fact, the Centre for Science and Environment (CSE) states that there has been a 52 percent spike in poaching and wildlife crimes between 2014 and 2016. The number of species that are poached or illegally traded in the country have gone up from 400 in 2014 to 465 in 2016.

Global Tiger Initiative, Save the Elephant, UNDP Sea Turtle Project, Wildlife Crime Initiative, Protecting Species, Wildlife Crime Control Bureau; and multiple private and government institutions are a few of the countless projects operating for wildlife conservation. Still, instead of a steady decline, every year poaching and killing seems to fluctuate.

The CSE states that 50 tigers were poached across Indian reserves in 2016, the highest number in a decade, despite the conservation efforts by the government and global institutions. Three hundred and forty Peacocks were poached between 2015 and 2016, amounting to a 193 percent rise over 2014.

In a wildlife bio reserve or nature reserve, rangers are able to patrol only small areas at any given point of time. Often understaffed, underequipped and poorly armed, these men go beyond the call of duty to pursue and apprehend wildlife poachers. But as the statistics show, it’s not always enough!

Adding to this is the evolution of poachers, who have mapped patrolling routes and avoid regular trails; they can anticipate animal movement and have resources to track their prey; they have advanced weapons as well as circumvent laws to stay protected. What can be done that’s humanly possible to counter poaching?

The answer does not necessarily lie simply in increasing human intervention, but probably in the use of better technology. Aerial-drones, infra-red cameras, real-time monitoring devices, RFID tags, GPS geo-location for surveillance and data collection are some of the devices that can be put to good use. If you combine this with the use of big data and analytics to process incoming data feeds and link it with AI, I believe a significant difference can be made.

This is not a long shot. Similar technologies are actively being tested and deployed across the globe for anti-poaching purposes.  

These technologies collate and process huge volumes of data such as poaching signs, wildlife observations, arrests and other patrol results that are logged in real-time on handheld devices by rangers in the field. The data, fed into a centralised analytics platform, is processed into charts, maps and reports on rangers’ movements, poaching arrests, deaths and causes, locations, animals being targeted, among multiple metrics to help standardise monitoring and enforcement.

Other technologies employ AI planning, machine learning, and behaviour modelling for wildlife protection. It is fed basic information about the protected area and information about previous patrolling routes and poaching activities to generate predictions of potential poaching locations and possible patrol routes. But that’s not all.

By integrating machine with AI, these technologies can also predict poachers’ behaviour, game-theoretic reasoning and route planning. They learn the behaviour models of the poachers from the crime data collected and calculate a randomized patrolling strategy to match the probabilities of poachers taking each route.

For a country like India, where there is poaching threat to animals like tigers, peacocks, elephants, leopards, blue bulls, blackbucks, rhinoceroses, chinkaras and spotted deer; a combination of Big-Data Analytics and AI that can collect and process wildlife crime data and predict poaching, will do wonders.

Such technology can be implemented in areas such as Kaziranga for Rhinos, Corbett, Sundarbans, Bandhavgarh and Ranthambore for Tigers, Periyar for Elephants, Gir for Lions, Kahna for Peacocks, among many others, which are dealing with inadequate wildlife protection infrastructure and staff. Going forward, sanctuaries that have been initiated with drones and advanced tracking gear might be able to leverage Big-Data analytic and AI tools to become the hunters instead of the hunted.