# Automated Pest Counting in Water Traps through Active Robotic Stirring for Occlusion Handling

arXiv: https://arxiv.org/abs/2510.21732 · HTML: https://mlsanity.com/paper/2510.21732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Contextual Transformer Networks for Visual Recognition](https://mlsanity.com/paper/2107.12292/) (2021)
- [Probabilistic Object Detection: Definition and Evaluation](https://mlsanity.com/paper/1811.10800/) (2018)
- [Omni-Dimensional Dynamic Convolution](https://mlsanity.com/paper/2209.07947/) (2022)
- [Crowd Counting with Decomposed Uncertainty](https://mlsanity.com/paper/1903.07427/) (2019)
- [An Intelligent Robotic System for Perceptive Pancake Batter Stirring and Precise Pouring](https://mlsanity.com/paper/2407.01755/) (2024)
- [Interactive Image-Based Aphid Counting in Yellow Water Traps under Stirring Actions](https://mlsanity.com/paper/2411.10357/) (2024)
- [Mixing by stirring: optimizing shapes and strategies](https://mlsanity.com/paper/2108.07064/) (2021)
- [Learning Multimodal Attention for Manipulating Deformable Objects with Changing States](https://mlsanity.com/paper/2309.14837/) (2023)
- [Robotic Optimization of Powdered Beverages Leveraging Computer Vision and Bayesian Optimization](https://mlsanity.com/paper/2409.11499/) (2024)
- [Counting with Confidence: Accurate Pest Monitoring in Water Traps](https://mlsanity.com/paper/2506.22438/) (2025)

## Abstract (arXiv)

Existing image-based pest counting methods rely on single static images and often produce inaccurate results under occlusion. To address this issue, this paper proposes an automated pest counting method in water traps through active robotic stirring. First, an automated robotic arm-based stirring system is developed to redistribute pests and reveal occluded individuals for counting. Then, the effects of different stirring patterns on pest counting performance are investigated. Six stirring patterns are designed and evaluated across different pest density scenarios to identify the optimal one. Finally, a heuristic counting confidence-driven closed-loop control system is proposed for adaptive-speed robotic stirring, adjusting the stirring speed based on the average change rate of counting confidence between consecutive frames. Experimental results show that the four circles is the optimal stirring pattern, achieving the lowest overall mean absolute counting error of 4.384 and the highest overall mean counting confidence of 0.721. Compared with constant-speed stirring, adaptive-speed stirring reduces task execution time by up to 44.7% and achieves more stable performance across different pest density scenarios. Moreover, the proposed pest counting method reduces the mean absolute counting error by up to 3.428 compared to the single static image counting method under high-density scenarios where occlusion is severe.