A curated list of papers, datasets, code, and pretrained models related to insect detection, classification, and monitoring using AI and deep learning.
Contributions are welcome! Please check the contribution guidelines below.
| Year | Reference/Description | Links |
|---|---|---|
| 2025 | [flatbug] Svenning A, et al. A General Method for Detection and Segmentation of Terrestrial Arthropods in Images. | paper code demo dataset |
| 2024 | Jain A, Cunha F, et al. A machine learning pipeline for automated insect monitoring | paper1 paper2 code dataset |
| 2023 | Bjerge K, et al. Accurate detection and identification of insects from camera trap images with deep learning. PLOS Sustainability and Transformation. | paper code weights dataset |
| 2023 | Stark T, et al. YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images. Scientific Reports. | paper project code model code weights |
| 2023 | Bjerge K, et al. Object detection of small insects in time-lapse camera recordings. Sensors. | paper |
| Year | Reference/Description | Links |
|---|---|---|
| 2025 | Gu J, et al. BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning | website paper1 code dataset |
| 2024 | Jain A, Cunha F, et al. A machine learning pipeline for automated insect monitoring | paper1 paper2 code dataset |
| Year | Reference/Description | Links |
|---|---|---|
| 2025 | [gbifxdl] Mougeot G, et al. GBIF eXtreme downloader - Scalable GBIF Image Downloading with Metadata | code |
| 2024 | [antenna] Antenna - The Insect Data Platform | website code paper |
| 2024 | [camtrapdp] Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data | website code paper |
| 2024 | [Mothbot] Mothbot - Offline Post-Processing and Labelling for Automated Insect Data | website code |
Contributions are welcome!
Please:
- Check for duplicates before adding a new entry.
- Follow the table format above.
- Include DOIs, GitHub links, and dataset sources if available.
- Open a pull request with a clear description of your addition.
This project is licensed under the MIT License.
🦋 "Small bugs, big data."