# Future of Edge AI in biodiversity monitoring

arXiv: https://arxiv.org/abs/2602.13496 · HTML: https://mlsanity.com/paper/2602.13496/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 205 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Builds on (most cited first, 30 of 31)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"](https://mlsanity.com/paper/1908.07201/) (2019)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Efficient Pipeline for Camera Trap Image Review](https://mlsanity.com/paper/1907.06772/) (2019)
- [Computational bioacoustics with deep learning: a review and roadmap](https://mlsanity.com/paper/2112.06725/) (2021)
- [BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning](https://mlsanity.com/paper/2505.23883/) (2025)
- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [Deep learning tools for the measurement of animal behavior in neuroscience](https://mlsanity.com/paper/1909.13868/) (2019)
- [Perch 2.0: The Bittern Lesson for Bioacoustics](https://mlsanity.com/paper/2508.04665/) (2025)
- [We Should Chart an Atlas of All the World's Models](https://mlsanity.com/paper/2503.10633/) (2025)
- [Edge Impulse: An MLOps Platform for Tiny Machine Learning](https://mlsanity.com/paper/2212.03332/) (2022)
- [Transferring Dense Pose to Proximal Animal Classes](https://mlsanity.com/paper/2003.00080/) (2020)
- [Machine Learning and Deep Learning -- A review for Ecologists](https://mlsanity.com/paper/2204.05023/) (2022)
- [The Search for Squawk: Agile Modeling in Bioacoustics](https://mlsanity.com/paper/2505.03071/) (2025)
- [Roadmap for Edge AI: A Dagstuhl Perspective](https://mlsanity.com/paper/2112.00616/) (2021)
- [TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors](https://mlsanity.com/paper/2407.21453/) (2024)
- [Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps](https://mlsanity.com/paper/2411.14467/) (2024)
- [Conservation Tools: The Next Generation of Engineering--Biology Collaborations](https://mlsanity.com/paper/2301.01103/) (2023)
- [Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML](https://mlsanity.com/paper/2504.12272/) (2025)
- [Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggers](https://mlsanity.com/paper/2509.20103/) (2025)
- [Send Less, Save More: Energy-Efficiency Benchmark of Embedded CNN Inference vs. Data Transmission in IoT](https://mlsanity.com/paper/2510.24829/) (2025)
- [Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides](https://mlsanity.com/paper/2412.12222/) (2024)
- [acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devices](https://mlsanity.com/paper/2501.17841/) (2025)
- [AniTrack: A Power-Efficient, Time-Slotted and Robust UWB Localization System for Animal Tracking in a Controlled Setting](https://mlsanity.com/paper/2506.00216/) (2025)

## Built on by (most cited first, 3 of 3)

- [Can Edge-Deployable Vision-Language Models Identify Species?](https://mlsanity.com/paper/2609.11916/) (2026)
- [Project SPARROW and the Future of Conservation Technology](https://mlsanity.com/paper/2606.00108/) (2026)
- [Cross-Modal Corroboration for Annotation-Free Wildlife Monitoring](https://mlsanity.com/paper/2606.21613/) (2026)

## 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.