The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool

Müller-Linow, Mark; Pinto, Francisco; Scharr, Hanno; Rascher, Uwe

doi:10.1186/s13007-015-0052-z

Cited by 85 publications

(74 citation statements)

References 45 publications

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“…This system consisted of two commercial digital single-lens reflex cameras (Canon EOS 5DMark II) equipped with 50 mm lenses and aligned in a fixed geometry with a baseline of approximately 200 mm between them (for details, see [41]). Both cameras were pointing to the target in nadir direction.…”

Section: Measuring Set Up and Data Acquisitionmentioning

confidence: 99%

“…This was achieved using the stereo imaging data and the software toolbox developed by Müller-Linow et al [41], which computes the surface of each leaf from a disparity map. The algorithm can be summarized in five basic steps: calibration of the stereo camera setup, correspondence analysis, 3D point cloud generation, leaf segmentation, and leaf surface reconstruction.…”

Section: Canopy 3d Reconstruction and Leaf Angle Estimationmentioning

confidence: 99%

“…In the following, we give a brief summary of these processing steps. A detailed description of this processing pipeline is given in [41].…”

Section: Canopy 3d Reconstruction and Leaf Angle Estimationmentioning

confidence: 99%

“…In the past years few methodologies, including structured light approaches [36], stereo imaging [37,38] and laser scanning techniques [39,40], have become available to estimate plant structural traits including leaf angle distribution. In particular, stereo imaging has been referred as a rapid and easy approach to characterize the outer canopy leaves, providing a good compromise between accuracy and versatility in the field [38,41]. The integration of this kind of data with spectral information represents a potential tool to address issues related with the effect of canopy structure on canopy fluorescence emission and photosynthesis.…”

Section: Introductionmentioning

confidence: 99%

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Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

et al. 2017

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Abstract:The effect that the canopy structure and the viewing geometry have on the intensity and the spatial distribution of passively measured sun-induced chlorophyll fluorescence at canopy scale is still not well understood. These uncertainties constrain the potential use of fluorescence to quantify photosynthesis at this level. Using a novel technique, we evaluated the diurnal changes in the spatial distribution of sun-induced fluorescence at 760 nm (F 760 ) within the canopy as a consequence of the spatial disposition of the leaves and the viewing angle of the sensor. High resolution spectral and stereo images of a full sugar beet canopy were recorded simultaneously in the field to estimate maps of F 760 and the surface angle distribution, respectively. A dedicated algorithm was used to align both maps in the post-processing and its accuracy was evaluated using a sensitivity test. The relative angle between sun and the leaf surfaces primarily determined the amount of incident Photosynthetic Active Radiation (PAR), which in turn was reflected in different values of F 760 , with the highest values occurring in leaf surfaces that are perpendicularly oriented to the sun. The viewing angle of the sensor also had an impact in the intensity of the recorded F 760 . Higher viewing angles generally resulted in higher values of F 760 . We attribute these changes to a direct effect of the vegetation directional reflectance response on fluorescence retrieval. Consequently, at leaf surface level, the spatio-temporal variations of F 760 were mainly explained by the sun-leaf-sensor geometry rather than directionality of the fluorescence emission. At canopy scale, the diurnal patterns of F 760 observed on the top-of-canopy were attributed to the complex interplay between the light penetration into the canopy as a function of the display of the various leaves and the fluorescence emission of each leaf which is modulated by the exposure of the individual leaf patch to the incoming light and the functional status of photosynthesis. We expect that forward modeling can help derive analytical simplified skeleton assumptions to scale canopy measurements to the leaf functional properties.

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Section: Measuring Set Up and Data Acquisitionmentioning

confidence: 99%

Section: Canopy 3d Reconstruction and Leaf Angle Estimationmentioning

confidence: 99%

“…In the following, we give a brief summary of these processing steps. A detailed description of this processing pipeline is given in [41].…”

Section: Canopy 3d Reconstruction and Leaf Angle Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

et al. 2017

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“…Several specialized algorithms have been developed to measure whole-plant properties and particularly plant size [6,17,25,27,31,40,55,61]. Nondestructive measurement of a plant's projected leaf area (PLA), i.e., the counting of plant pixels from top-view images, is considered a good approximation of plant size for rosette plants and is currently used.…”

Section: Introductionmentioning

confidence: 99%

Leaf segmentation in plant phenotyping: a collation study

Scharr

Minervini

French

et al. 2015

Machine Vision and Applications

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Image-based plant phenotyping is a growing application domain of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first of its kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge (LSC) of the Computer Vision Problems in Plant Phenotyping (CVPPP) workshop in 2014. Four methods are presented: three segment leaves via processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be achieved with satisfactory accuracy (>90% Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Besides, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (http://www.plantphenotyping.org/CVPPP2014-dataset) to support future challenges beyond segmentation within this application domain.

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Robust mosaicking of maize fields from aerial imagery

et al. 2020

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Premise Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high‐throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field‐scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery. Methods We have developed an improved mosaicking pipeline, Video Mosaicking and summariZation (VMZ), which uses a novel two‐dimensional mosaicking algorithm that minimizes errors in estimating the transformations between successive frames during registration. The VMZ pipeline uses only the imagery, rather than relying on vehicle telemetry, ground control points, or global positioning system data, to estimate the frame‐to‐frame homographies. It exploits the spatiotemporal ordering of the image frames to reduce the computational complexity of finding corresponding features between frames using feature descriptors. We compared the performance of VMZ to a standard two‐dimensional mosaicking algorithm (AutoStitch) by mosaicking imagery of two maize (Zea mays) research nurseries freely flown with a variety of trajectories. Results The VMZ pipeline produces superior mosaics faster. Using the speeded up robust features (SURF) descriptor, VMZ produces the highest‐quality mosaics. Discussion Our results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management.

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The leaf angle distribution of natural plant populations: assessing the canopy with a novel software tool

Cited by 85 publications

References 45 publications

Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

Multiangular Observation of Canopy Sun-Induced Chlorophyll Fluorescence by Combining Imaging Spectroscopy and Stereoscopy

Leaf segmentation in plant phenotyping: a collation study

Robust mosaicking of maize fields from aerial imagery

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