COMPUTER VISION PROBLEMS IN PLANT PHENOTYPING (CVPPP)

http://www.plant-phenotyping.org/CVPPP2014

Zurich, September 12, 2014, in conjunction with ECCV 2014 (http://eccv2014.org/)

The goal of this workshop is to showcase computer vision challenges in plant phenotyping. Plant phenotyping is the identification of effects on the phenotype (ie., the plant appearance and behavior) as a result of genotype differences (ie., differences in the genetic code) and the environmental conditions a plant has been exposed to. Knowing the plant phenotypes is a key ingredient for knowledge-based bioeconomy and this, not only literally helps in the efforts to feed the world, but is also essential for feed, fibre and fuel production.

While previously, collection of phenotypic traits was manual, currently noninvasive, imaging-based methods are increasingly utilized in plant phenotyping and resulting images need to be analyzed in a high throughput, robust, accurate, and reliable manner. The occurring problems differ from usual tasks addressed by the computer vision community due to the requirements posed by this application scenario.

Plants are complex, self-changing systems with complexity increasing over time. Typical problems in measuring their actual properties comprise measuring size, shape, 3d surface structure, architecture, and other structural traits of plants and their organs (leaves, fruits, roots etc.) or plant populations, where core problems are e.g. reliable detection and multi-label segmentation of many similar objects, or reconstruction of specular, almost featureless, discontinuous surfaces. When interested in changes over time, for example growth rates, tracking, optical flow or scene flow estimation are required. Inherently the tracked objects change their appearance over time. In some cases images may be acquired under controlled conditions, but typically are taken in challenging environments occurring in the field, in green houses, employing automated and/or affordable acquisition setups. Unfortunately, without automated and accurate computer vision to extract the phenotypes, a bottleneck is encountered, hampering our understanding of plant biology.

This conference includes papers presenting:

List of workshop papers

12: Texture-Based Leaf Identification, Milan Šulc, Czech Technical University in Prague, Jiří Matas, Czech Technical University in Prague [PDF]
13: A model-based approach to recovering the structure of a plant from images, Ben Ward, University of Adelaide, John Bastian, University of Adelaide, Anton Van Den Hengel, University of Adelaide, Daniel Pooley, University of Adelaide, Lachlan Fleming, University of Adelaide, Rajendra Bari, Bayer CropScience, Bettina Berger, University of Adelae, Mark Tester, King Abdullah University of Science and Technology [PDF]
16: Surface Reconstruction of Plant Shoots from Multiple Views, Michael Pound, University of Nottingham, Andrew French, University of Nottingham, UK, Erik Murchie, University of Nottingham, Tony Pridmore, University of Nottingham [PDF]
17: Generation and application of hyperspectral 3D plant models, Jan Behmann, University Bonn, Anne-Katrin Mahlein, University Bonn, Stefan Paulus, University Bonn, Erich-Christian Oerke, University Bonn, Heiner Kuhlmann, University Bonn, Lutz Plümer, University Bonn [PDF]
18: Visual Object Tracking for the Extraction of Multiple Interacting Plant Root Systems, Stefan Mairhofer, University of Nottingham, Craig Sturrock, University of Nottingham, Malcolm Bennett, University of Nottingham, Sacha Mooney, University of Nottingham, Tony Pridmore, University of Nottingham [PDF]
19: Image-based Phenotyping of the Mature Arabidopsis Shoot System, Marco Augustin, Vienna University of Technology, Yll Haxhimusa, Vienna University of Technology, Wolfgang Busch, Austrian Academy of Sciences, Walter Kropatsch, Vienna University of Technology [PDF]
20: Hybrid Consensus Learning for Legume Species and Cultivars Classification, Monica Larese, CIFASIS-CONICET, Pablo Granitto, CIFASIS, Argentina [PDF]
21: 3D multimodal simulation of image acquisition by X-Ray and MRI for validation of seedling measurements with segmentation algorithms, Landry Benoit, Université d'Angers, Georges Semaan, Université d'Angers, Etienne Belin, Université d'Angers, François Chapeau-Blondeau, Université d'Angers, Didier Demilly, SNES-GEVES, David Rousseau, Univ. of Lyon, France [PDF]
23: High-Resolution Plant Phenotypes from Multi-View Stereo Reconstruction, Maria Klodt, Technical University Munich, Daniel Cremers, Technical University Munich [PDF]
24: 3D Plant Modeling: Localization, Mapping and Segmentation for Plant Phenotyping Using a Single Hand-held Camera, Thiago Santos, Embrapa, Luciano Koenigkan, Embrapa, Jayme Barbedo, Embrapa, Gustavo Rodrigues, Embrapa [PDF]
26: A Crop/Weed Field Image Dataset for the Evaluation of Computer Vision Based Precision Agriculture Tasks, Sebastian Haug, Robert Bosch GmbH, Jörn Ostermann, TNT, Leibniz University Hannover [PDF]
27: Representing Roots on the Basis of Reeb Graphs in Plant Phenotyping, Ines Janusch, Vienna University of Technology, Walter Kropatsch, Vienna University of Technology, Wolfgang Busch, Austrian Academy of Sciences, Daniela Ristova, Austrian Academy of Sciences [PDF]
28: 3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants, Jean-Michel Pape, Leibniz Institute of Plant Genetics and Crop Plant Research, Christian Klukas, Leibniz Institute of Plant Genetics and Crop Plant Research [PDF]
31: Distortion Correction in 3D-Modeling of Roots for Plant Phenotyping, Tushar Kanta Das Nakini, ViGIR, University of Missouri, Guilherme DeSouza, University of Missouri [PDF]