Future of Edge AI in biodiversity monitoring
Not on the map. Why?
Abstract (arXiv)
1. Many ecological decisions are slowed by the gap between collecting and analysing biodiversity data. Edge computing moves processing closer to the sensor, with edge artificial intelligence (AI) enabling on-device inference, reducing reliance on data transfer and continuous connectivity. In principle, this shifts biodiversity monitoring from passive logging towards autonomous, responsive sensing systems. In practice, however, adoption remains fragmented, with key architectural trade-offs, performance constraints, and implementation challenges rarely reported systematically. 2. Here, we analyse 82 studies published between 2017 and 2025 that implement edge computing for biodiversity monitoring across acoustic, vision, tracking, and multi-modal systems. We synthesise hardware platforms, AI model optimisation, and wireless communication to critically assess how design choices shape ecological inference, deployment longevity, and operational feasibility. 3. Publications increased from 3 in 2017 to 19 in 2025. We identify four system types: (I) TinyML, low-power microcontrollers (MCUs) for single-taxon or rare-event detection; (II) Edge AI, single-board computers (SBCs) for multi-species classification and real-time alerts; (III) Distributed edge AI; and (IV) Cloud AI for retrospective processing pipelines. Each system type represents context-dependent trade-offs among power consumption, computational capability, and communication requirements. 4. Our analysis reveals the evolution of edge computing systems from proof-of-concept to robust, scalable tools. We argue that edge computing offers opportunities for responsive biodiversity management, but realising this potential requires increased collaboration between ecologists, engineers, and data scientists to align model development and system design with ecological questions, field constraints, and ethical considerations.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 31 of its 205 references that are in the map. First posted on arXiv 13 Feb 2026.
Builds on
31 of 205 references.
References · 31
- 2015Distilling the Knowledge in a Neural Networkcited by 10,076
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
- 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
- 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
- 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 161
- 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 129
- 2019Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"cited by 106
- 2019Machine Learning at the Network Edge: A Surveycited by 83
- 2019Efficient Pipeline for Camera Trap Image Reviewcited by 79
- 2021Computational bioacoustics with deep learning: a review and roadmapcited by 78
- 2025BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learningcited by 48
19 more
- 2019Deep learning tools for the measurement of animal behavior in neurosciencecited by 44
- 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 44
- 2025We Should Chart an Atlas of All the World's Modelscited by 25
- 2025Perch 2.0: The Bittern Lesson for Bioacousticscited by 25
- 2022Edge Impulse: An MLOps Platform for Tiny Machine Learningcited by 24
- 2020Transferring Dense Pose to Proximal Animal Classescited by 23
- 2022Machine Learning and Deep Learning -- A review for Ecologistscited by 11
- 2025The Search for Squawk: Agile Modeling in Bioacousticscited by 8
- 2021Roadmap for Edge AI: A Dagstuhl Perspectivecited by 4
- 2024TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensorscited by 4
- 2024Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Trapscited by 3
- 2023Conservation Tools: The Next Generation of Engineering--Biology Collaborationscited by 2
- 2025Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyMLcited by 2
- 2025Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggerscited by 2
- 2025Send Less, Save More: Energy-Efficiency Benchmark of Embedded CNN Inference vs. Data Transmission in IoTcited by 2
- 2024Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsidescited by 1
- 2025acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devicescited by 1
- 2025AniTrack: A Power-Efficient, Time-Slotted and Robust UWB Localization System for Animal Tracking in a Controlled Settingcited by 1
- 2025Sequencing on Silicon: AI SoC Design for Mobile Genomics at the Edgecited by 1
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Citing papers per year: 2026: 3.
3 papers.