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Top Pest Control Datasets

From classifying mice and rodent species to counting insects, computer vision improves the pest control industry's accuracy and proficiency of its' services.

Case study: https://blog.roboflow.com/rabbit-deterrence-system/

Indoor Scene Recognition

Examples of Images
From the official dataset page:
Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g., bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information.

Database

The database contains 67 Indoor categories ... The number of images varies across categories, but there are at least 100 images per category. All images are in jpg format. The images provided here are for research purposes only.

Paper

A. Quattoni, and A.Torralba. Recognizing Indoor Scenes. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.

Acknowledgments

Thanks to Aude Oliva for helping to create the database of indoor scenes.
                            Funding for this research was provided by NSF Career award (IIS 0747120)
                            

This dataset was originally created by Lao and 时光. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/gugugu/pests-f8kkr.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This dataset was originally created by Nirmani. To see the current project, which may have been updated since this version, please go here: https://universe.roboflow.com/nirmani/yolo-custome-925.

This dataset is part of RF100, an Intel-sponsored initiative to create a new object detection benchmark for model generalizability.

Access the RF100 Github repo: https://github.com/roboflow-ai/roboflow-100-benchmark

This Workspace is for pest data for training of 15 classes of pest.

These are the 15 classes of pests ( Done pests will be marked by ✅):

  • ash weevil ✅
  • blister beetle ✅
  • fruit fly ✅
  • fruit sucking moth ✅
  • helicoverpa
  • hellula
  • plutella xylostella
  • leaf webber
  • leucinodes
  • mealy bug ✅
  • pieris brassicae caterpillar ✅
  • root grubs ✅
  • schizaphis gramium ✅
  • uroleucon compositae ✅
  • whitefly ✅