# Automatic Moth Detection from Trap Images for Pest Management

arXiv: https://arxiv.org/abs/1602.07383 · HTML: https://mlsanity.com/paper/1602.07383/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2016.

## Built on by (most cited first, 10 of 10)

- [Automated Pest Detection with DNN on the Edge for Precision Agriculture](https://mlsanity.com/paper/2108.00421/) (2021)
- [Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification](https://mlsanity.com/paper/2307.15427/) (2023)
- [GCNet: Probing Self-Similarity Learning for Generalized Counting Network](https://mlsanity.com/paper/2302.05132/) (2023)
- [AlertTrap: A study on object detection in remote insects trap monitoring system using on-the-edge deep learning platform](https://mlsanity.com/paper/2112.13341/) (2021)
- [A Case for Rejection in Low Resource ML Deployment](https://mlsanity.com/paper/2208.06359/) (2022)
- [Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning](https://mlsanity.com/paper/2304.02291/) (2023)
- [BOLLWM: A real-world dataset for bollworm pest monitoring from cotton fields in India](https://mlsanity.com/paper/2304.00763/) (2023)
- [Comparison between transformers and convolutional models for fine-grained classification of insects](https://mlsanity.com/paper/2307.11112/) (2023)
- [Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images](https://mlsanity.com/paper/2404.03474/) (2024)
- [Locally Grouped and Scale-Guided Attention for Dense Pest Counting](https://mlsanity.com/paper/2408.16503/) (2024)

## Abstract (arXiv)

Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort. It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required.