Learning to Count Leaves in Rosette Plants

Giuffrida, Mario Valerio; Minervini, Massimo; Tsaftaris, Sotirios A.

doi:10.5244/c.29.cvppp.1

Cited by 84 publications

(126 citation statements)

References 29 publications

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“…However, our recent experience in organizing the leaf segmentation (Section 3.3.3) and leaf counting (Section 3.3.5) contests (see Section 4) using a subset of the annotated data presented here showed that the number of available images and criteria used allowed computer vision scientists to work successfully on these problems (see e.g. Klukas, 2014, 2015;Giuffrida et al, 2015)). …”

Section: Computer Vision Tasks and Datasetsmentioning

confidence: 99%

Finely-grained annotated datasets for image-based plant phenotyping

Minervini

Fischbach

Scharr

et al. 2016

Pattern Recognition Letters

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248

209

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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.

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Section: Computer Vision Tasks and Datasetsmentioning

confidence: 99%

Finely-grained annotated datasets for image-based plant phenotyping

Minervini

Fischbach

Scharr

et al. 2016

Pattern Recognition Letters

Self Cite

248

209

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“…Learning based algorithms are known to generalize Figure 6: Effects of training data size on a learning algorithm for leaf counting [13].…”

Section: Resultsmentioning

confidence: 99%

“…As an example to demonstrate this fact, we use a learning to count leaves application with regression [13]. Image patches are extracted in the log-polar domain and are provided to K-means to build a codebook.…”

Section: Resultsmentioning

confidence: 99%

An interactive tool for semi-automated leaf annotation

Minervini¹,

Giuffrida²,

Tsaftaris³

2015

Procedings of the Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015

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High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust computer vision algorithms that analyze images and extract phenotyping information to be associated with genotypes and environmental conditions for identifying traits suitable for further development. Obtaining leaf level quantitative data is important towards understanding better this interaction. While certain efforts have been made to obtain such information in an automated fashion, further innovations are necessary. In this paper we present an annotation tool that can be used to semi-automatically segment leaves in images of rosette plants. This tool, which is designed to exist in a stand-alone fashion but also in cloud based environments, can be used to annotate data directly for the study of plant and leaf growth or to provide annotated datasets for learning-based approaches to extracting phenotypes from images. It relies on an interactive graph-based segmentation algorithm to propagate expert provided priors (in the form of pixels) to the rest of the image, using the random walk formulation to find a good per-leaf segmentation. To evaluate the tool we use standardized datasets available from the LSC and LCC 2015 challenges, achieving an average leaf segmentation accuracy of almost 97% using scribbles as annotations. The tool and source code are publicly available at http://www.phenotiki.com and as a GitHub repository at https://github.com/phenotiki/LeafAnnotationTool.

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“…Tobacco images acquired in our scenario as well as Arabidopsis images, both of them accompanied by ground truth segmentations and annotations, are available to the public as a benchmark dataset [9]. While for Arabidopsis results steadily improve [26,27,28], tobacco leaf segmentation and counting remains challenging [29]. Here, we use a previously developed multi-level procedure.…”

Section: Perception Modulementioning

confidence: 99%

A cognitive architecture for automatic gardening

Agostini

Aleny

Fischbach

et al. 2017

Computers and Electronics in Agriculture

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Keywords: cognitive architecture, optimized plant treatments, automatic gardening, human-robot interaction, learning planning operators. AbstractIn large industrial greenhouses, plants are usually treated following well established protocols for watering, nutrients, and shading/light. While this is practical for the automation of the process, it does not tap the full potential for optimal plant treatment. To more efficiently grow plants, specific treatments according to the plant individual needs should be applied. Experienced human gardeners are very good at treating plants individually. Unfortunately, hiring a crew of gardeners to carry out this task in large greenhouses is not cost effective. In this work we present a cognitive system that integrates artificial intelligence (AI) techniques for decision-making with robotics techniques for sensing and acting to autonomously treat plants using a real-robot platform. Artificial intelligence techniques are used to decide the amount of water and nutrients each plant needs according to the history of the plant. Robotic techniques for sensing measure plant attributes (e.g. leaves) from visual information using 3D model representations. These attributes are used by the AI system to make decisions about the treatment to apply. Acting techniques execute robot movements to supply the plants with the specified amount of water and nutrients.

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Learning to Count Leaves in Rosette Plants

Cited by 84 publications

References 29 publications

Finely-grained annotated datasets for image-based plant phenotyping

Finely-grained annotated datasets for image-based plant phenotyping

An interactive tool for semi-automated leaf annotation

A cognitive architecture for automatic gardening

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