The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition
Abstract
Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper, we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75, 000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In addition, we annotate about 19, 000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upperlevel category. Furthermore, we perform several baseline experiments on the IP102 dataset, including handcrafted and deep feature based classification methods. Experimental results show that this dataset has the challenges of interand intra- class variance and data imbalance. We believe our IP102 will facilitate future research on practical insect pest control, fine-grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/xpwu95/IP102.
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Plant diseases and pests detection based on deep learning: a review
Liu, J., & Wang, X. (2021) · Plant Methods · doi:10.1186/s13007-021-00722-9 · Cited by 1,011
Deep learning and computer vision will transform entomology
Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C., & Raitoharju, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002545117 · Cited by 469
Insect classification and detection in field crops using modern machine learning techniques
Kasinathan, T., Singaraju, D., & Uyyala, S. R. (2021) · Information Processing in Agriculture · doi:10.1016/j.inpa.2020.09.006 · Cited by 316
Phung, V. H., & Rhee, E. J. (2019) · Applied Sciences · doi:10.3390/app9214500 · Cited by 283
Karar, M. E., Alsunaydi, F., Albusaymi, S., & Alotaibi, S. (2021) · Alexandria Engineering Journal · doi:10.1016/j.aej.2021.03.009 · Cited by 253
Metadata
- Authors
- Wu, X., Zhan, C., Lai, Y.-K., Cheng, M.-M., & Yang, J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 8779-8788
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2966002724
- Cited by
- 541
- References
- 14
- FWCI
- 57.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wu, X., Zhan, C., Lai, Y.-K., Cheng, M.-M., & Yang, J. (2019). IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00899
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