Insect detection: Datasets & Models

Manual insect scouting is slow, requires trained agronomists, and doesn't scale across large acreage or many trap sites, so infestations are often caught late. Automating detection lets growers monitor pest pressure continuously and trigger targeted interventions before damage spreads, reducing unnecessary blanket spraying. A computer vision model can process thousands of trap images consistently without fatigue, standardizing counts that would otherwise vary from one scout to another. For agriculture operations, this turns sporadic manual checks into a repeatable data pipeline.

Detection gives you per-insect counts and locations rather than a single yes/no answer, which supports threshold-based spray decisions and population trend tracking. The workflow runs on existing infrastructure like trap cameras and phones, so you can start from a labeled insect dataset and deploy without new hardware. It scales from a single greenhouse to distributed field networks, and RF-DETR handles the dense, small-object scenes common in trap imagery.

1. Upload an image: Drop any photo into the widget above to try insect detection for free, right on this page.
2. Preview instant results: The model detects aphid, beetle, whitefly, moth, fruit fly within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.