A Fully Automated Three-Stage Procedure for Spatio-Temporal Leaf Segmentation with Regard to the B-Spline-Based Phenotyping of Cucumber Plants

Harmening, Corinna; Paffenholz, Jens‐André

doi:10.3390/rs13010074

Cited by 8 publications

(8 citation statements)

References 37 publications

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“…It could potentially be used in highthroughput phenotypic data acquisition. In the existing research [19,20,25,27,56], the point cloud data of the early-growth plant was used to divide the leaves with simple plant structures. This study directly segmented the sugar beet plants with different complexities of the plant leaf layout at different growth stages in the field with obvious leaf overlapping.…”

Section: Discussionmentioning

confidence: 99%

“…The extracted phenotypic data verified the effectiveness of the segmentation methods. Harmening et al [19] proposed a fully automatic leaf segmentation method to segment the time series 3D point clouds of cucumber plants acquired by an automotive grade laser scanner. It revealed the consistency with maximal deviations in the determined leaf areas up to 5%.…”

Section: Introductionmentioning

confidence: 99%

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Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

et al. 2022

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Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R2 (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

et al. 2022

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“…Hence, they are used in several engineering geodetic applications, either for geometric state descriptions (cf. references [25][26][27]) or in the context of monitoring tasks ( [28,29]). In addition to their flexibility, B-spline surfaces have two other properties that qualify them for use within an areal deformation analysis:…”

Section: B-spline Surfaces For Deformation Analysismentioning

confidence: 99%

Laser Scanner–Based Deformation Analysis Using Approximating B-Spline Surfaces

2021

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Due to the increased use of areal measurement techniques, such as laser scanning in geodetic monitoring tasks, areal analysis strategies have considerably gained in importance over the last decade. Although a variety of approaches that quasi-continuously model deformations are already proposed in the literature, there are still a multitude of challenges to solve. One of the major interests of engineering geodesy within monitoring tasks is the detection of absolute distortions with respect to a stable reference frame. Determining distortions and simultaneously establishing the joint geodetic datum can be realised by modelling the differences between point clouds acquired in different measuring epochs by means of a rigid body movement that is superimposed by distortions. In a previous study, we discussed the possibility of estimating these rigid body movements from the control points of B-spline surfaces approximating the acquired point clouds. Alternatively, we focus on estimating them by means of constructed points on B-spline surfaces in this study. This strategy has the advantage of larger redundancy compared to the control point–based strategy. Furthermore, the strategy introduced allows for the detection of rigid body movements between point clouds of different epochs and for the simultaneous localisation of areas in which the rigid body movement is superimposed by distortions. The developed approach is based on B-spline models of epoch-wise acquired point clouds, the surface parameters of which define point correspondences on different B-spline surfaces. Using these point correspondences, a RANSAC-approach is used to robustly estimate the parameters of the rigid body movement. The resulting consensus set initially defines the non-distorted areas of the object under investigation, which are extended and statistically verified in a second step. The developed approach is applied to simulated data sets, revealing that distorted areas can be reliably detected and that the parameters of the rigid body movement can be precisely and accurately determined by means of the strategy.

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“…While a few studies have developed leaf tracking methods on 3D reconstructions (e.g. cotton [ 25 ], cucumber [ 26 ], tomato [ 27 ] and maize [ 28 , 27 ], most of them have only been validated on limited datasets that may not reflect actual HTP conditions (thousands of plants, large genetic diversity and growing scenarios). Moreover, these datasets were often limited to young plants which are less challenging to analyse than in later growth stages (emergence of the reproductive organs, more occlusion due to leaf crossings, more frequent disappearance of leaves due to senescence).…”

Section: Introductionmentioning

confidence: 99%

PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time

et al. 2022

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Background High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. Results We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10–355 plants. Conclusions We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.

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A Fully Automated Three-Stage Procedure for Spatio-Temporal Leaf Segmentation with Regard to the B-Spline-Based Phenotyping of Cucumber Plants

Cited by 8 publications

References 37 publications

Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

Segmentation of Individual Leaves of Field Grown Sugar Beet Plant Based on 3D Point Cloud

Laser Scanner–Based Deformation Analysis Using Approximating B-Spline Surfaces

PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time

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