# A Deep Learning-based Pest Insect Monitoring System for Ultra-low Power Pocket-sized Drones

arXiv: https://arxiv.org/abs/2407.00815 · HTML: https://mlsanity.com/paper/2407.00815/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [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)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-drones](https://mlsanity.com/paper/2301.12175/) (2023)
- [Squeezed Edge YOLO: Onboard Object Detection on Edge Devices](https://mlsanity.com/paper/2312.11716/) (2023)
- [Sim-to-Real Vision-depth Fusion CNNs for Robust Pose Estimation Aboard Autonomous Nano-quadcopter](https://mlsanity.com/paper/2308.01833/) (2023)

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

- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [An Efficient Ground-aerial Transportation System for Pest Control Enabled by AI-based Autonomous Nano-UAVs](https://mlsanity.com/paper/2502.14455/) (2025)
- [Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge](https://mlsanity.com/paper/2609.11939/) (2026)

## Abstract (arXiv)

Smart farming and precision agriculture represent game-changer technologies for efficient and sustainable agribusiness. Miniaturized palm-sized drones can act as flexible smart sensors inspecting crops, looking for early signs of potential pest outbreaking. However, achieving such an ambitious goal requires hardware-software codesign to develop accurate deep learning (DL) detection models while keeping memory and computational needs under an ultra-tight budget, i.e., a few MB on-chip memory and a few 100s mW power envelope. This work presents a novel vertically integrated solution featuring two ultra-low power System-on-Chips (SoCs), i.e., the dual-core STM32H74 and a multi-core GWT GAP9, running two State-of-the-Art DL models for detecting the Popillia japonica bug. We fine-tune both models for our image-based detection task, quantize them in 8-bit integers, and deploy them on the two SoCs. On the STM32H74, we deploy a FOMO-MobileNetV2 model, achieving a mean average precision (mAP) of 0.66 and running at 16.1 frame/s within 498 mW. While on the GAP9 SoC, we deploy a more complex SSDLite-MobileNetV3, which scores an mAP of 0.79 and peaks at 6.8 frame/s within 33 mW. Compared to a top-notch RetinaNet-ResNet101-FPN full-precision baseline, which requires 14.9x more memory and 300x more operations per inference, our best model drops only 15% in mAP, paving the way toward autonomous palm-sized drones capable of lightweight and precise pest detection.