A stereo imaging system for measuring structural parameters of plant canopies.

By Bernhard Biskup, Hanno Scharr, U. Schurr, Uwe Rascher. 2007

211 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ecology, Plant Science

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Citations in the corpus

Finely-grained annotated datasets for image-based plant phenotyping (2016, background): “Additional depth information as can be computed from a pair of images with di ﬀ erent focus (Favaro and Soatto, 2007) of the Arabidopsis dataset or stereo images (Biskup et al., 2007) of the Tobacco dataset, to be released in the future, may further facilitate segmentation (Dellen et al., 2011).”
A Review of Imaging Techniques for Plant Phenotyping (2014, methodology): “The available imaging sensor technologies used for the 3D mapping of plants at present are primarily light detection and ranging (LIDAR) (or laser scanner) sensors [161], stereo vision [39,82], photon mixer devices (PMD) time-of-flight cameras [79] and even consumer-gaming interface Microsoft Kinect”
Plant phenotyping: from bean weighing to image analysis (2015, background): “3D-reconstruction already works in the lab [75-77], but is challenging to be reliably performed in the field [78].”
Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images (2015, methodology): “Known approaches use time-lapse image data, 3-D information as input or images are acquired under well-defined conditions [1, 9, 18].”
Field-based phenomics for plant genetics research (2012, background): “Image analysis can improve separation of background (usually soil) from the plant canopy and open myriad options for quantifying plant architecture (Biskup et al., 2007), flower or fruit numbers (Adamsen et al., 2000; Thorp and Dierig, 2011) and other traits.”
The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool (2015, methodology): “Steeper leaf parts are not well visible and thus do not give reliable angle estimates [27].”
segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017, background): “The use of color in combination with depth images or multiple images for supervised or unsupervised plant segmentation is also common practice [4,10,29,46,49,51,52,57].”
Leaf segmentation in plant phenotyping: a collation study (2015, background): “The use of colour in combination with depth images or multiple images for supervised or unsupervised plant segmentation is also common practice [4,10,27,44,47,49,50,56].”
High-Throughput Estimation of Crop Traits: A Review of Ground and Aerial Phenotyping Platforms (2020, methodology): “Due to the distance between a person’s eyes, the two images formed at the fundi are basically similar but slightly different, and there is a certain disparity [130].”
Annotated Image Datasets of Rosette Plants (2014, background): “Additional depth information as can be computed from stereo images [35] for the tobacco dataset, to be released in the future, may further facilitate segmentation [36, 37, 38, 39].”
REVIEW: PART OF A HIGHLIGHT ON BREEDING STRATEGIES FOR FORAGE AND GRASS IMPROVEMENT Advanced phenotyping offers opportunities for improved breeding of forage and turf species (2012, background): “Key words: Forage, turf, breeding, phenotyping, growth, biomass, imaging, marker-assisted selection, remote sensing.”