Finely-grained annotated datasets for image-based plant phenotyping

By Massimo Minervini, Andreas Fischbach, Hanno Scharr, Sotirios A. Tsaftaris. 2016

306 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Ecology, Environmental Engineering, Plant Science, Evaluation & Benchmarks

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Citations in the corpus

Recurrent Neural Networks for Semantic Instance Segmentation (2017, methodology): “Models trained for the CVPPP dataset are evaluated with the symmetric best dice (SBD) and the difference in count (DiC) metric, as in [29].”
Deep Watershed Transform for Instance Segmentation (2016, background): “, Pascal VOC [8], BSDS500 [17], and CVPPP [19]) Cityscapes has a large number of scenes involving dozens of instances with large degrees of occlusions at vastly different scales.”
Segment Anything (2023, background): “-20 0 +20 +40 IoU delta at 1 center point GTEA [34, 63] TrashCan [52] DRAM [24] PIDRay [104] Cityscapes [25] WoodScape [112] IBD [17] EgoHOS [113] Plittersdorf [46] VISOR [28, 27] NDISPark [22, 23] Hypersim [86] OVIS [81] ADE20K [117] iShape [111] ZeroWaste-f [6] STREETS [91] LVIS [44] NDD20 [100] T”
Recent advances in convolutional neural networks (2015, background): “There are already some fine-grained image datasets (such as Birds [178], Dogs [179], Cars [180], and Plants [181]).”
End-to-End Instance Segmentation and Counting with Recurrent Attention (2016, methodology): “One instance segmentation benchmark is the CVPPP plant leaf dataset [12], which was developed due to the importance of instance segmentation in plant phenotyping.”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “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].”
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review (2020, background): “In particular, an Arabidopsis dataset with finely grained annotations has been developed to open opportunities for the development of advanced analysis methods [81].”
SAM 2: Segment Anything in Images and Videos (2024, unknown): “…Li et al., 2015), iShape (Yang et al., 2021a), NDD20 (Trotter et al., 2020), NDISPark (Ciampi et al., 2021, 2022), OVIS (Qi et al., 2022), PPDLS (Minervini et al., 2016), Plittersdorf (Haucke et al., 2022), STREETS (Snyder & Do, 2019), TimberSeg (Fortin et al., 2022), TrashCan (Hong et al.,…”
Leaf segmentation in plant phenotyping: a collation study (2015, methodology): “Such data are publicly available in the form of a curated dataset [35], and a software tool was released to facilitate leaf annotation [36].”
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks (2017, methodology): “Further description of the dataset and the methodology used in its construction is provided in the publication (Minervini et al., 2015).”
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, unknown): “In a previous study [108], the authors introduced the first dataset for computer vision-based plant phenotyping, which was made available in a separate report [109].”
Multi-modality imagery database for plant phenotyping (2016, background): “Due to diverse variations of leaf shape, appearance, layout, growth and movement, plant image analysis is a non-trivial computer vision task [Minervini et al., 2015].”