A Large-Scale In-the-wild Dataset for Plant Disease Segmentation
The University of Queensland · University of Southern Queensland · +6 more institutions
Abstract
Plant diseases pose significant threats to agriculture, making proper diagnosis and effective treatment crucial for protecting crop yields. In automatic diagnosis processing, image segmentation helps to identify and localize diseases. Developing robust image segmentation models for detecting plant diseases requires high-quality annotations. Unfortunately, existing datasets rarely include segmentation labels and are typically confined to controlled laboratory settings, which fail to capture the complexity of images taken in the wild. Motivated by these, we established a large-scale segmentation dataset for plant diseases, dubbed PlantSeg. In particular, PlantSeg is distinct from existing datasets in three key…
Citation impact
- FWCI
- 26.35
- Percentile
- 99%
- References
- 35
Authors
6- TWTianqi WeiCorresponding
The University of Queensland
- ZCZhi Chen
University of Southern Queensland
- XYXin Yu
Australian Institute of Business, The University of Adelaide
- SCSL Chapman
The University of Queensland, Agriculture and Food
- PMPaul Melloy
Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food, Animal, Food and Health Sciences
Topics & keywords
- Segmentation
- Plant disease
- Benchmarking
- Image segmentation
- Scale-space segmentation
- Pattern recognition (psychology)
- Key (lock)
- Zero hunger