Wildlife monitoring is critical for ecological study, conservation, and wildlife management, but traditional approaches have drawbacks. The combination of unmanned aerial vehicles (UAVs) with machine learning (ML) offers a viable approach to overcoming the limits of traditional wildlife monitoring methods and improving wildlife management and conservation tactics. The combination of UAVs and ML provides efficient and effective solutions for wildlife monitoring. UAVs with high-resolution cameras record airborne footage, while machine learning algorithms automate animal detection, tracking, and behavior analysis. The chapter discusses challenges, limitations, and future directions in using UAVs and ML for wildlife monitoring, addressing regulatory, technical, and ethical considerations, and emphasizing the need for ongoing research and technological advancements. Overall, the integration of UAVs and ML provides a promising solution to overcome the limitations of traditional wildlife monitoring methods and enhance wildlife management and conservation strategies.
Top1. Introduction
Wildlife monitoring is critical to scientific development and the decision-making process for wildlife protection (He et al., 2016). Collaboration efforts over a wide range of geographies and timelines provide vital insights into dynamic animal behavior, the impact of human activities, environmental changes, and critical aspects of wildlife, ecology, and the environment. As the effects of human-caused environmental change become more pronounced, large-scale collaborative wildlife surveillance is required to manage and safeguard animal resources successfully (Markovchick-Nicholls et al., 2008). To that aim, an integrated platform for collecting, analyzing, and managing large-scale multi-modal wildlife sensor data over the Internet infrastructure is critical. Technological advancements have found extensive application in wildlife monitoring and tracking various species (Xu et al., 2016). Various sensor-based devices are commonly used to relay animal data to a central station. However, monitoring animals in remote and expansive wildlife areas presents challenges due to hazardous conditions and unpredictable movement patterns. Additionally, conventional sensor networks face limitations in timely data transmission due to energy constraints, making it costly and impractical.
Various wildlife monitoring methods can be broadly categorized into Camera Traps, GPS Tracking, Remote Sensing, and Unmanned Aerial Vehicles (UAVs) (Nicheporchuk et al., 2020). UAVs have gained popularity in diverse applications due to their versatility, high speed, and durability. They offer different imaging techniques by integrating pictures from varying flight altitudes, enabling coverage of extensive areas, and producing detailed images (Šimek et al., 2017). UAVs act as independent mobile data collectors, gathering time-sensitive information. When utilized for animal monitoring, UAVs effectively overcome geographical challenges while ensuring no negative impact on the observed animals (Xu et al., 2016). In addition, UAVs use revolutionary monitoring techniques, such as group recognition and gender determination using visual or thermal imaging, to revolutionize ecological research.
Machine learning (ML) is highly effective in creating dependable correlations in data-driven systems through experiential learning. This knowledge derived from data can be tailored to tackle novel challenges and analyze unexplored data. The availability of abundant datasets from diverse spatiotemporal scales in wildlife monitoring is vital for the successful application of AI techniques through machine learning and interpretation (Sharma et al., 2022). ML empowers IT systems to autonomously identify patterns, establish rules, and develop solutions(Abang Abdurahman et al., 2022). Machine learning technology is occasionally used to detect and distinguish animals, as well as to recognize crucial behaviors such as running and walking, utilizing just seismic data generated by animals (Szenicer et al., 2022).
UAVs play a crucial role in efficient data collection and communication across various sectors. The integration of ML has advanced UAV operations, leading to improved automation and accuracy in tasks like communications, sensing, and data collection (Kurunathan et al., 2022). Integrating UAVs with ML enables additional functionalities like image processing, trajectory planning, and monitoring UAV-aided communication. ML enhances controlled mobility and trajectory decisions, making UAVs suitable for various AI-driven IoT paradigms. This integration adds an AI layer to existing UAV-enabled monitoring applications, enabling feature extraction and prediction capabilities. It creates new opportunities in real-time monitoring, data collection, and prediction across domains(Nahla, 2021) (Rashid, 2010). However, when applying ML to UAVs for wildlife conservation, there are significant challenges. These include obtaining high-quality data in remote settings, optimizing ML models for real-time processing and adaptability to diverse wildlife behaviors, and addressing privacy and ethical concerns. Other complex challenges involve energy-efficient UAV operation, reliable communication, multi-UAV coordination, algorithm complexity, cost-effectiveness, regulatory compliance, interoperability, and ensuring long-term sustainability through careful resource management and planning (Mona, 2020) (Nada, 2021).