IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition
By Xiaoping Wu, Chi Zhan, Yu-Kun Lai, Ming-Ming Cheng, Jufeng Yang. Computer Vision and Pattern Recognition, 2019
472 citations recorded by the source corpus.
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.
Subfields: Plant Science, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/cdc4730a37c9f9add9092a04da762f9abac5e988
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Plant Science
Citations in the corpus
- Deep learning and computer vision will transform entomology (2020, background): “Classification accuracy is generally much lower when the insects are recorded live in their 271 natural environments (85, 86), but when class confidence is low at the species-level, it may still be 272 possible to confidently classify insects to a coarser taxonomic resolution (87).”
- Plant diseases and pests detection based on deep learning: a review (2021, background): “IP102: Insect Pest Recognition Database: 75,000 images belonging to 102 categories [91] Field https ://githu b.”