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Combating Poaching with AI: A New Frontier in Wildlife Conservation

With each passing day, the world's most vulnerable species are pushed closer to the brink of extinction as poaching continues to threaten their survival. In this race against time, artificial intelligence (AI) has emerged as a powerful ally for conservationists in the fight to save endangered wildlife. By harnessing cutting-edge AI-driven solutions, we can detect and deter poachers more effectively, enabling the protection of vast habitats and the animals that call them home. In this article, we explore the different ways AI is used to prevent poaching and highlight a new project by Robotto, a company dedicated to harnessing the power of AI to support conservation efforts.

Unleashing AI's Power Against Poachers
1. Seeing the Unseen: Camera Traps and Image Recognition

In recent years, poaching has reached alarming levels, decimating the populations of countless endangered species such as elephants, rhinoceroses, tigers, and pangolins. This illegal practice not only threatens the survival of these species, but it also has cascading effects on ecosystems and local communities, disrupting delicate ecological balances and undermining the livelihoods of people who depend on sustainable tourism and ecosystem services. As traditional anti-poaching measures struggle to keep pace with the scale of the problem, the need for innovative solutions has become increasingly apparent. Artificial intelligence (AI) has emerged as a powerful ally for conservationists in the fight to save endangered wildlife by offering new ways to detect and deter poachers effectively and efficiently.

One of the earliest applications of AI in combating poaching involves camera traps, which use motion sensors to capture images of wildlife and potential poachers (Schroeder, 2018). Hack the Planet, for instance, deployed an AI-powered system in Gabon in 2021 that detects both animals and poachers in real-time using fixed cameras (Hack the Planet, 2021). By reducing false alarms and the time spent reviewing footage, AI-enhanced camera traps provide a more efficient surveillance system for conservationists.

2. Anticipating Danger: AI-Driven Predictive Modeling

Predictive modeling has shown great promise in helping conservationists anticipate and prevent poaching incidents. In a groundbreaking study in Uganda, researchers used AI algorithms to analyze past poaching data, successfully predicting the locations and timings of future poaching activities with remarkable accuracy (Wato et al., 2016). The information was then used to inform targeted anti-poaching patrols, leading to a substantial reduction in poaching events in the area. By optimizing the deployment of limited resources, predictive modeling can enhance the effectiveness of anti-poaching efforts.

3. Soaring to New Heights: Advanced Drone Technology

Unmanned aerial vehicles (UAVs), or drones, have emerged as a valuable tool for monitoring vast and remote protected areas. Equipped with AI-driven object recognition, drones can detect both wildlife and potential poachers, allowing park rangers to respond more rapidly to threats (Mulero-Pázmány et al., 2014). Robotto's innovative OmniSight drone solution uses UAV technology, providing real-time monitoring and detection from a dynamic point of view, as opposed to the fixed-camera solutions employed by other organizations.

Navigating the Challenges: Limitations and Ethical Considerations

Despite the promise of AI-driven solutions, there are potential drawbacks and limitations. For instance, human reliance on these technologies may lead to complacency or a lack of situational awareness. Additionally, the lack of an existing UAV point-of-view database could hamper the efficiency of AI algorithms.

Ethical considerations include potential privacy concerns, such as fixed cameras using facial recognition without informing individuals. However, Robotto's UAV technology mitigates this concern, as it does not collect GDPR-relevant data.

Revolutionizing Wildlife Conservation: Robotto's Trailblazing Solution

Robotto's NatureTrackAI software, which includes autonomous flight, animal detection, and flight path modification, enables park rangers to work proactively and safely to protect wildlife. The software uses AI to detect animals and track their movements, allowing rangers to intervene, if necessary, without resorting to disruptive methods like firecrackers. This technology, combined with human detection capabilities, offers a powerful tool for combating poaching.

Artificial intelligence is playing a pivotal role in the fight against poaching, with applications ranging from camera traps and image recognition to predictive modeling and drone technology. As a pioneer in this field, Robotto is poised to make a significant impact on wildlife conservation by combining NatureTrackAI software with human detection capabilities. By embracing these innovative AI-driven solutions, we can better protect endangered species and preserve our planet's biodiversity for future generations.

Citations: Fang, F., Stone, P., & Tambe, M. (2016). When security games go green: Designing defender strategies to prevent poaching and illegal fishing. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (pp. 286-293). IJCAI.

Gholami, S., Ford, A., White, M., & Clulow, S. (2017). A framework for supervised classification in presence of mislabeled training data: An application to presence-absence species distribution modeling. Ecosphere, 8(12), e02035.

Hack the Planet. (2021). The Hack the Planet system. Retrieved from

Mulero-Pázmány, M., Stolper, R., Van Essen, L. D., Negro, J. J., & Sassen, T. (2014). Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS One, 9(1), e83873.

Mulero-Pázmány, M., Jenni-Eiermann, S., Strebel, N., Sattler, T., Negro, J. J., & Tablado, Z. (2018). Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review. PLoS One, 13(6), e0196736.

Schroeder, R. (2018). Saving wildlife with AI camera traps. National Geographic. Retrieved from

Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C. (2015). Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Scientific Data, 2, 150026.

Wato, Y. A., Wahungu, G. M., & Okello, M. M. (2016). Application of the exponential random graph models in the assessment of patterns of elephant poaching in a wildlife conservation area in Kenya. Ecological Modelling, 339, 80-88.


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