Finely-grained annotated datasets for image-based plant phenotyping

Abstract

Graphical AbstractFinely-grained annotated datasets for image-based plant phenotyping Massimo Minervini, Andreas Fischbach, Hanno Scharr, Sotirios A. TsaftarisIn 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. Research Highlights• 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. ABSTRACTImage-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.

Leaf segmentation in plant phenotyping: a collation study

“…Both types of information will help in resolving occlusions and obtaining better boundaries. Currently, such data are not publicly available but we are working to release such curated datasets and dedicated annotation tools to the community [38,37].…”

A Big Data Based Edge Detection Method for Image Pattern Recognition - A Survey

“…Pascal Fua and Yvan G. Leclerc, describes standard edge detection method and it fails to find most of the relavent edges so author proposed the new method for images in initial segmentation [9]. Massimo Minervini , Andreas Fischbach , Hanno Scharr , Sotirios A. Tsaftaris, describes a collection of benchmark datasets of raw and annotated top-view color images of rosette plants which describe plant material, imaging setup and procedures for different experiments: [10]. Eren Erdal Aksoy, Alexey Abramov, Florentin Wörgötter, Hanno Scharr, Andreas Fischbach, Babette Dellen, preprocessing, leaf segmentation, and leaf tracking.…”

Machine Learning for Plant Phenotyping Needs Image Processing

“…Thus, recent efforts to lower the entry barrier and accelerate this process were aimed at releasing open access data together with suitable performance evaluation protocols (see [11,12] and http://www.plant-phenotyping.org/datasets). Preprint of an article accepted for publication.…”