Drones and AI: Revolutionizing Wildlife Monitoring and Conservation

Climate change, habitat loss, and poaching pose significant threats to wildlife conservation. Traditional monitoring methods are often labor-intensive and limited in scope. The integration of drone technology and artificial intelligence (AI) offers a transformative approach, enabling unprecedented precision, efficiency, and scalability in wildlife research. Drones provide high-resolution aerial imagery and real-time data collection, while AI transforms raw data into actionable insights for automated animal detection, behavioral analysis, population estimation, and habitat assessment. This review explores the recent advancements in drone and AI applications for wildlife monitoring, focusing on operational frameworks, AI techniques, and practical implementations across diverse wildlife research domains, as well as highlighting current limitations and potential improvements in drone and AI synergy.

This review investigates the applications, challenges, and future potential of drones and AI-driven solutions in wildlife monitoring. The literature search was conducted across multidisciplinary databases, including IEEE Xplore, Springer Link, Wiley Online Library, MDPI, and ScienceDirect. Peer-reviewed articles, reviews, and surveys published between 2018 and 2025 were considered, focusing on the application of drones and AI in wildlife monitoring. The search strategy utilized predefined inclusion criteria, combining keywords and Boolean operators. The collected literature was categorized into thematic domains, including automatic species identification, tracking and movement analysis, anti-poaching and surveillance, population estimation, and habitat analysis.

Modern wildlife monitoring employs diverse drone types, each tailored to specific study requirements. Multi-copters excel in precision tasks, while fixed-wing drones are optimized for tracking migratory species or mapping vast areas. Hybrid VTOL models merge these advantages. Autonomous drones equipped with RTK-GNSS technology deliver exceptional precision. Remotely operated vehicles (ROVs) are used for marine ecosystem studies. Drones are typically equipped with specialized instruments, including visible cameras, thermal cameras, multispectral and hyperspectral cameras, LiDAR, acoustic sensors, radio telemetry receivers, and gas sensors. These instruments enable the collection of diverse data for ecological research and conservation efforts.

Machine learning (ML) algorithms, including supervised, unsupervised, and reinforcement learning, are used to analyze wildlife data. Deep learning (DL) algorithms, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), are suitable for image processing. CNNs serve as the backbone for most modern object detection systems, including two-stage detectors (e.g., R-CNN, Fast R-CNN, Mask R-CNN) and single-stage detectors (e.g., YOLO). These algorithms enable automated animal detection, behavioral analysis, population estimation, and habitat assessment.

Drones and AI are used for automatic species identification through image and acoustic data analysis. AI algorithms can identify species based on visual characteristics or vocalizations. Tracking and movement analysis involves using drones to follow animal movements and behaviors. GPS collars and radio telemetry receivers on drones enable the tracking of migratory and endangered species. AI algorithms analyze movement patterns and predict future behaviors.

Drones equipped with thermal cameras and AI algorithms are used for anti-poaching surveillance. These systems can detect poachers and illegal activities in protected areas. Population estimation involves using drones to count animals and estimate population sizes. AI algorithms automate the counting process and improve accuracy. This data is crucial for conservation planning and management.

Drones equipped with multispectral and hyperspectral cameras are used for habitat analysis. These systems can assess vegetation health, water quality, and other environmental factors. LiDAR is used to analyze canopy density and measure tree height. AI algorithms analyze habitat data and identify areas for conservation and restoration.

Despite the advancements in drone and AI technologies, several challenges and limitations remain. These include the methodological approaches for monitoring small, cryptic, or nocturnal species; the robustness of AI-driven solutions in overcoming real-world challenges like occlusion in dense habitats and real-time processing in resource-constrained environments; and the insufficient discussion on integrating these technologies with complementary tools. Regulatory constraints and ethical considerations also pose challenges to the widespread adoption of drone and AI technologies in wildlife monitoring.

Future research directions include improving AI algorithms for species identification and behavior analysis, developing more robust drone platforms for diverse environments, and integrating drone and AI technologies with other conservation tools. Opportunities exist for developing standardized datasets and metrics for wildlife monitoring, promoting collaboration between researchers and conservation practitioners, and addressing regulatory and ethical concerns. The integration of AI with edge computing on drones will enable real-time data processing and autonomous decision-making.

The convergence of drone technology and artificial intelligence is reshaping wildlife monitoring, offering novel solutions to tackle challenges in species identification, animal tracking, anti-poaching, population estimation, and habitat analysis. By synthesizing insights from recent advancements, this review provides researchers with a structured framework for leveraging drone and AI systems in wildlife monitoring, identifying best practices, and outlining actionable pathways for future innovation in the field. Continued research and development in this area will enhance wildlife monitoring and conservation efforts.