AI Steps In as Elephants, Tigers and Humans Clash: What a Global Review Reveals

Across the farmlands of India, the villages of Nepal and the rangelands of Botswana, the collision between expanding human populations and wildlife is intensifying. A new systematic review published in Environmental Science and Pollution Research has pulled together the global evidence on human–wildlife conflict, cataloguing where it happens, which species are involved, what drives it, and how well modern technology can blunt its impact. The review, led by Lokesh Ganpat Kamde of Yeshwantrao Chavan College of Engineering in Nagpur, India, together with Sandip S. Khedkar and Sharad S. Chaudhari, followed the PRISMA 2020 guidelines, the internationally recognized standard for systematic reviews, to ensure that the evidence base was assembled and analyzed in a transparent and reproducible way.

The scale of the problem documented by the review is striking. The most frequently reported incidents of human–wildlife conflict fall into three categories: crop raiding, in which wild animals destroy standing food and cash crops; depredation on livestock, where carnivores and other species kill domesticated animals; and direct attacks on people, sometimes with fatal outcomes. These are not isolated events but recurring patterns that shape the daily routines, economic security and psychological wellbeing of communities living at the edge of protected areas. The review found that the species most often entangled in these conflicts are elephants, tigers, monkeys and crocodiles, a roster that spans some of the largest and most dangerous animals on Earth.

Why does this conflict persist and worsen? The review identifies a cluster of contributing factors, chief among them agricultural expansion, habitat fragmentation and human settlement encroaching on wildlife corridors. As farms push toward forest boundaries and settlements spread across former habitat, the buffer zones that once separated people from large mammals shrink to nothing. Elephants, which can consume hundreds of kilograms of vegetation daily, find cultivated fields irresistible; tigers and leopards follow prey and livestock into human-dominated landscapes. The review also highlights governance as a double-edged factor: strong governance emerged as a major contributor in the analysis, reflecting how policy decisions about land use, compensation schemes and protected-area management directly shape the frequency and severity of conflict on the ground.

The consequences ripple far beyond a trampled maize field or a lost goat. Studies cited in the review document livelihood insecurity among households adjacent to protected areas in Kenya, Ethiopia and Nepal, alongside psychological stress and eroded support for conservation. In some regions, communities report that compensation schemes are slow, bureaucratic or inadequate, which deepens resentment. The review notes that conflict between people and wildlife is often, at its root, conflict between groups of people, over land, resources and whose interests the state protects. That framing matters, because it suggests that purely technical fixes will never be sufficient without attention to the social and institutional dimensions of the problem.

Into this fraught landscape, a wave of technology has arrived. The review’s central contribution is its assessment of how artificial intelligence, deep learning, machine learning and sensor-based systems are being deployed to detect, track and predict wildlife incursions before they become tragedies. Camera traps paired with convolutional neural networks now identify individual tigers and elephants in real time. Systems built on the YOLO object-detection architecture flag animals approaching villages and send alerts to rangers and residents. Satellite remote sensing and geographic information systems map conflict hotspots, while Internet of Things devices and short-message-service networks deliver early warnings directly to farmers’ phones.

The quantitative heart of the review is a meta-analysis comparing the effectiveness of these technological approaches. Deep learning techniques scored highest, with an effectiveness value of 0.83, followed by machine learning at 0.76, AI-based systems at 0.72 and sensor-based technologies at 0.58. The hierarchy is instructive. Deep learning’s advantage stems from its ability to extract subtle features from raw imagery, distinguishing a tiger’s stripe pattern or an elephant’s silhouette under poor lighting, through dense rain or across thermal infrared feeds. Machine learning approaches that rely on hand-engineered features perform well but require more careful calibration. Sensor-only systems, such as pressure pads or tripwire fences, remain useful but generate more false alarms and offer less contextual information.

Real-world deployments illustrate the promise. In Xishuangbanna, Yunnan, China, AI monitoring and warning systems were applied to wild Asian elephants for the first time, tracking herds as they moved toward villages. In Bhutan, an AI-based animal intrusion detection system was developed specifically for human–wildlife conflict scenarios. Around Kaziranga National Park in Assam, India, researchers combined YOLO-based detection with attention mechanisms to protect both wildlife and people. In Kenya, machine learning has been applied to conflict mitigation planning, while drone-based tracking of tigers demonstrates how aerial platforms extend the reach of ground-based sensors. Edge computing architectures, which process data locally on devices rather than in distant data centers, are emerging as a way to deliver alerts with minimal latency even in areas with poor connectivity.

Yet the review is careful not to oversell the technology. Detection is only the first step in a chain that must end with a human response: a ranger dispatched, a village warned, a fence reinforced. Systems that identify an elephant at the forest boundary at two in the morning are worthless if no one receives or acts on the alert. The review also points to persistent challenges, including the cost of hardware, the difficulty of maintaining equipment in remote and humid environments, the need for large and locally relevant training datasets, and questions of privacy and ethics raised by surveillance networks that watch both animals and people. Non-lethal tools such as electric fencing, scare devices and deterrents remain important complements, and studies of low-cost electric fencing in smallholder agriculture show that traditional measures still have a role alongside the digital ones.

The deeper lesson of the review is that no single intervention, however sophisticated, can resolve human–wildlife conflict on its own. The authors conclude that integrated approaches, combining advanced technologies with ecological conservation and genuine community engagement, offer the most sustainable path to minimizing conflict and securing long-term coexistence. That means pairing a deep-learning camera network with habitat restoration that gives elephants somewhere else to go, and with compensation schemes that farmers actually trust. It means involving local communities in the design and operation of monitoring systems so that technology is perceived as a shared tool rather than an external imposition. Studies from Uganda, Namibia and Zimbabwe cited in the review consistently show that community perceptions determine whether mitigation measures succeed or fail.

As climate change shifts species ranges and infrastructure development fragments remaining habitat, the pressure at the human–wildlife interface will only grow. The review’s synthesis offers a roadmap: map the hotspots with satellites, watch the boundaries with intelligent cameras, warn the villages in real time, and underpin it all with fair governance and conservation policy that treats coexistence as a design goal rather than an accident. The effectiveness scores in the meta-analysis, from deep learning’s 0.83 down to sensor systems’ 0.58, are not just rankings but a guide for where conservation dollars and engineering effort will pay off fastest. If the past decade was about proving that machines can recognize an elephant in the dark, the next one will be about weaving those machines into landscapes where people and wildlife can both survive, and perhaps even thrive, on shared ground.

Subject of Research: Technological mitigation of human–wildlife conflict involving crop raiding, livestock depredation and attacks by elephants, tigers, monkeys and crocodiles

Article Title: Human–wildlife conflict and technological mitigation strategies: a systematic review

Article References: Kamde, L. G., Khedkar, S. S., & Chaudhari, S. S. (2026). Human–wildlife conflict and technological mitigation strategies: a systematic review. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-026-38232-7

Image Credits: AI Generated

DOI: 10.1007/s11356-026-38232-7

Keywords: human–wildlife conflict, artificial intelligence, deep learning, machine learning, wildlife conservation, early warning systems, elephants, tigers, crop raiding, habitat fragmentation, camera traps, meta-analysis