Name
Abstract | ||
|---|---|---|
First comprehensive annotated datasets for computer vision tasks in plant phenotyping.Publicly available data and evaluation criteria for eight challenging tasks.Tasks include fine-grained categorization of age, developmental stage, and cultivars.Example test cases and results on plant and leaf-wise segmentation and leaf counting. In this paper we present a collection of benchmark datasets for the development and evaluation of computer vision and machine learning algorithms in the context of plant phenotyping. We provide annotated imaging data and suggest suitable evaluation criteria for plant/leaf segmentation, detection, tracking as well as classification and regression problems. The Figure symbolically depicts the data available together with ground truth segmentations and further annotations and metadata.Display Omitted Image-based approaches to plant phenotyping are gaining momentum providing fertile ground for several interesting vision tasks where fine-grained categorization is necessary, such as leaf segmentation among a variety of cultivars, and cultivar (or mutant) identification. However, benchmark data focusing on typical imaging situations and vision tasks are still lacking, making it difficult to compare existing methodologies. This paper describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants. We briefly describe plant material, imaging setup and procedures for different experiments: one with various cultivars of Arabidopsis and one with tobacco undergoing different treatments. We proceed to define a set of computer vision and classification tasks and provide accompanying datasets and annotations based on our raw data. We describe the annotation process performed by experts and discuss appropriate evaluation criteria. We also offer exemplary use cases and results on some tasks obtained with parts of these data. We hope with the release of this rigorous dataset collection to invigorate the development of algorithms in the context of plant phenotyping but also provide new interesting datasets for the general computer vision community to experiment on. Data are publicly available at http://www.plant-phenotyping.org/datasets. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.patrec.2015.10.013 | Pattern Recognition Letters |
Keywords | Field | DocType |
Image processing,Machine vision and scene understanding,Plant biology,Annotated datasets | Computer vision,Categorization,Annotation,Use case,Segmentation,Computer science,Image processing,Raw data,Ground truth,Test case,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
81 | C | 0167-8655 |
Citations | PageRank | References |
18 | 1.03 | 30 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Minervini | 1 | 85 | 6.96 |
| Andreas Fischbach | 2 | 31 | 2.40 |
| Hanno Scharr | 3 | 430 | 37.92 |
| Sotirios A. Tsaftaris | 4 | 361 | 43.26 |