Dataset Information

Registration of spatio-temporal point clouds of plants for phenotyping.

ABSTRACT: Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.

SUBMITTER: Chebrolu N

PROVIDER: S-EPMC7906482 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Registration of spatio-temporal point clouds of plants for phenotyping.

Chebrolu Nived N Magistri Federico F Läbe Thomas T Stachniss Cyrill C

PloS one 20210225 2

Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. Thi ...[more]

PMID: 33630896

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Project description:With the introduction of multi-camera systems in modern plant phenotyping new opportunities for combined multimodal image analysis emerge. Visible light (VIS), fluorescence (FLU) and near-infrared images enable scientists to study different plant traits based on optical appearance, biochemical composition and nutrition status. A straightforward analysis of high-throughput image data is hampered by a number of natural and technical factors including large variability of plant appearance, inhomogeneous illumination, shadows and reflections in the background regions. Consequently, automated segmentation of plant images represents a big challenge and often requires an extensive human-machine interaction. Combined analysis of different image modalities may enable automatisation of plant segmentation in "difficult" image modalities such as VIS images by utilising the results of segmentation of image modalities that exhibit higher contrast between plant and background, i.e. FLU images. For efficient segmentation and detection of diverse plant structures (i.e. leaf tips, flowers), image registration techniques based on feature point (FP) matching are of particular interest. However, finding reliable feature points and point pairs for differently structured plant species in multimodal images can be challenging. To address this task in a general manner, different feature point detectors should be considered. Here, a comparison of seven different feature point detectors for automated registration of VIS and FLU plant images is performed. Our experimental results show that straightforward image registration using FP detectors is prone to errors due to too large structural difference between FLU and VIS modalities. We show that structural image enhancement such as background filtering and edge image transformation significantly improves performance of FP algorithms. To overcome the limitations of single FP detectors, combination of different FP methods is suggested. We demonstrate application of our enhanced FP approach for automated registration of a large amount of FLU/VIS images of developing plant species acquired from high-throughput phenotyping experiments.

Project description:BackgroundLaserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.ResultsA surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy.ConclusionWe introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of high throughput phenotyping.Highlights• Automatic classification of plant organs using geometrical surface information• Transfer of analysis methods for low resolution point clouds to close-up laser measurements of plants• Analysis of 3D-data requirements for automated plant organ classification.

Project description:This study investigated the spatio-temporal properties of blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signals in gray matter, excluding the confounding, inaccurate contributions of large blood vessels. We quantified the spatial specificity of the BOLD response, and we investigated whether this specificity varies as a function of time from stimulus onset. fMRI was performed at 7 Tesla (T), where mapping signals of parenchymal origin are easily detected. Two abutting visual stimuli were adjusted to elicit responses centered on a flat gray matter region in V1. fMRI signals were sampled at high-resolution orthogonal to the retinotopic boundary between the representations of the stimuli. Signals from macro-vessels were masked out. Principal component analysis revealed that the first component in space accounted for 96.2+/-1.6% of the variance over time. The spatial profile of this time-invariant response was fitted with a model consisting of the convolution of a step function and a Gaussian point-spread-function (PSF). The mean full-width at half-maximal-height of the fitted PSF was 2.34+/-0.20 mm. Based on simulations of confounding effects, we estimate that BOLD PSF in human gray matter is smaller than 2 mm. A time-point to time-point analysis revealed that the PSF obtained during the 3rd (1.52 mm) and 4th (1.99 mm) seconds of stimulation were narrower than the mean PSF obtained from the 5th second on (2.42+/-0.15 mm). The position of the edge of the responding region was offset (1.72+/-0.07 mm) from the boundary of the stimulated region, indicating a spatial non-linearity. Simulations showed that the effective contrast between active and non-active columns is reduced 25-fold when imaged using a PSF whose width is equal to the cycle of the imaged columnar organization. Thus, the PSF of the hyper-oxygenated BOLD response in human gray matter is narrower than that reported at 1.5 T, where macro-vessels dominate the mapping signals. The initial phase of this response is more spatially specific than later phases. Data acquisition methods that suppress macro-vascular signals should increase the spatial specificity of BOLD fMRI. The choice of optimal stimulus duration represents a trade-off between the spatial specificity and the overhead associated with short stimulus duration.

Dataset Information

Registration of spatio-temporal point clouds of plants for phenotyping.

Publications

Registration of spatio-temporal point clouds of plants for phenotyping.

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