Future of edge AI in biodiversity monitoring

Abstract Many ecological decisions are slowed by the gap between collecting and analysing biodiversity information. Edge computing moves data processing closer to the sensor, with edge artificial intelligence (AI) using AI models for data processing and selective data transfer. edge AI extends the scope of biodiversity monitoring by complementing retrospective ecological analysis with real‐time sensing, enabling responsive and adaptive decision‐making. However, the adoption of edge AI systems in ecological research remains fragmented because deploying efficient and reliable systems is complex. Edge hardware, on‐device AI models and network configuration are tightly linked, such that optimising any one component shapes the performance and feasibility of the others, demanding holistic system design. For the uptake of edge AI in biodiversity monitoring, it is essential to understand the design trade‐offs, performance constraints and implementation challenges faced by current systems. Here, we analyse 82 studies published between 2017 and 2025 that use edge AI systems for biodiversity monitoring across acoustic, vision‐based, tracking and multi‐modal sensing. We synthesise hardware platforms, AI model optimisation and wireless communication to assess how these design choices influence ecological inference and determine the practicality and longevity of field deployments. Annual publications rise moderately from 3 in 2017 to 19 in 2025. Across these studies, we identify four system types that represent distinct patterns of edge computing adoption in ecological monitoring. These include low‐power microcontrollers (MCUs) for single‐taxon or rare‐event detection ( Type I: TinyML ), single‐board computers (SBCs) supporting multi‐species classification and real‐time alerts ( Type II: edge AI ), distributed systems coordinating processing across multiple devices ( Type III: Distributed edge AI ) and network‐connected devices for cloud processing ( Type IV: Cloud AI ). Each type illustrates different trade‐offs among power consumption, computational capability and communication requirements. Our analysis reveals the evolution of edge AI systems from proof‐of‐concept to robust, scalable tools. We argue that edge AI systems enable timely and scalable ecological observations, but realising their full potential depends on closer collaboration between ecologists, engineers and data scientists to align AI model development and system design with ecological needs and field realities.