# Towards Scalable Insect Monitoring: Ultra-Lightweight CNNs as On-Device Triggers for Insect Camera Traps

arXiv: https://arxiv.org/abs/2411.14467 · HTML: https://mlsanity.com/paper/2411.14467/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding](https://mlsanity.com/paper/2311.15206/) (2023)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [Filtering Empty Camera Trap Images in Embedded Systems](https://mlsanity.com/paper/2104.08859/) (2021)
- [Insect Identification in the Wild: The AMI Dataset](https://mlsanity.com/paper/2406.12452/) (2024)
- [Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications](https://mlsanity.com/paper/2311.07163/) (2023)

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

- [EV-Flying: an Event-based Dataset for In-The-Wild Recognition of Flying Objects](https://mlsanity.com/paper/2506.04048/) (2025)
- [Future of Edge AI in biodiversity monitoring](https://mlsanity.com/paper/2602.13496/) (2026)
- [Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priors](https://mlsanity.com/paper/2508.20089/) (2025)

## Abstract (arXiv)

Camera traps, combined with AI, have emerged as a way to achieve automated, scalable biodiversity monitoring. However, the passive infrared (PIR) sensors that trigger camera traps are poorly suited for detecting small, fast-moving ectotherms such as insects. Insects comprise over half of all animal species and are key components of ecosystems and agriculture. The need for an appropriate and scalable insect camera trap is critical in the wake of concerning reports of declines in insect populations. This study proposes an alternative to the PIR trigger: ultra-lightweight convolutional neural networks running on low-powered hardware to detect insects in a continuous stream of captured images. We train a suite of models to distinguish insect images from backgrounds. Our design achieves zero latency between trigger and image capture. Our models are rigorously tested and achieve high accuracy ranging from 91.8% to 96.4% AUC on validation data and >87% AUC on data from distributions unseen during training. The high specificity of our models ensures minimal saving of false positive images, maximising deployment storage efficiency. High recall scores indicate a minimal false negative rate, maximising insect detection. Further analysis with saliency maps shows the learned representation of our models to be robust, with low reliance on spurious background features. Our system is also shown to operate deployed on off-the-shelf, low-powered microcontroller units, consuming a maximum power draw of less than 300mW. This enables longer deployment times using cheap and readily available battery components. Overall we offer a step change in the cost, efficiency and scope of insect monitoring. Solving the challenging trigger problem, we demonstrate a system which can be deployed for far longer than existing designs and budgets power and bandwidth effectively, moving towards a generic insect camera trap.