# A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management

arXiv: https://arxiv.org/abs/2509.08822 · HTML: https://mlsanity.com/paper/2509.08822/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 138 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Intelligence at the Extreme Edge: A Survey on Reformable TinyML](https://mlsanity.com/paper/2204.00827/) (2022)
- [Audio-based identification of beehive states](https://mlsanity.com/paper/1811.06330/) (2018)
- [Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination](https://mlsanity.com/paper/2205.04675/) (2022)
- [TinyVers: A Tiny Versatile System-on-chip with State-Retentive eMRAM for ML Inference at the Extreme Edge](https://mlsanity.com/paper/2301.03537/) (2023)
- [Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices](https://mlsanity.com/paper/2412.09289/) (2024)
- [To bee or not to bee: Investigating machine learning approaches for beehive sound recognition](https://mlsanity.com/paper/1811.06016/) (2018)
- [MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees](https://mlsanity.com/paper/2311.10876/) (2023)
- [Varroa destructor detection on honey bees using hyperspectral imagery](https://mlsanity.com/paper/2403.14359/) (2024)
- [The Ethics of Biosurveillance](https://mlsanity.com/paper/2111.11712/) (2021)
- [IntelliBeeHive: An Automated Honey Bee, Pollen, and Varroa Destructor Monitoring System](https://mlsanity.com/paper/2309.08955/) (2023)
- [Computer Vision Approaches for Automated Bee Counting Application](https://mlsanity.com/paper/2406.08898/) (2024)

## Abstract (arXiv)

Honey bee colonies are essential for global food security and ecosystem stability, yet they face escalating threats from pests, diseases, and environmental stressors. Traditional hive inspections are labor-intensive and disruptive, while cloud-based monitoring solutions remain impractical for remote or resource-limited apiaries. Recent advances in Internet of Things (IoT) and Tiny Machine Learning (TinyML) enable low-power, real-time monitoring directly on edge devices, offering scalable and non-invasive alternatives. This survey synthesizes current innovations at the intersection of TinyML and apiculture, organized around four key functional areas: monitoring hive conditions, recognizing bee behaviors, detecting pests and diseases, and forecasting swarming events. We further examine supporting resources, including publicly available datasets, lightweight model architectures optimized for embedded deployment, and benchmarking strategies tailored to field constraints. Critical limitations such as data scarcity, generalization challenges, and deployment barriers in off-grid environments are highlighted, alongside emerging opportunities in ultra-efficient inference pipelines, adaptive edge learning, and dataset standardization. By consolidating research and engineering practices, this work provides a foundation for scalable, AI-driven, and ecologically informed monitoring systems to support sustainable pollinator management.