Segmentation of lettuce in coloured 3D point clouds for fresh weight estimation

Mortensen, Anders Krogh; Bender, Asher; Whelan, Brett; Barbour, Margaret M.; Sukkarieh, Salah; Karstoft, Henrik; Gislum, René

doi:10.1016/j.compag.2018.09.010

Cited by 50 publications

(39 citation statements)

References 29 publications

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“…Sugar beet grows very fast from stage T1 to T2 and is gradually aged from stage T2 to T3. There were other phenotypic traits involving plant area, such as leaf project area and surface area, that have been used to estimate biomass [29]. The correlations between these phenotypic traits and biomass may be lower than between total leaf area and biomass, but it is easier to measure.…”

Section: Estimation Of Biomass Using Plant Phenotypic Traitsmentioning

confidence: 99%

“…The convex hull and the concave hull are used to describe the volume [36,50]. As researches have shown that the concave hull has poor performance for biomass estimation [29], we only studied the correlation between the convex hull and biomass. Similar to the total leaf area, the estimation of stages T1-T2 is better than that of T1-T3, but the difference is not significant.…”

Section: Estimation Of Biomass Using Plant Phenotypic Traitsmentioning

confidence: 99%

“…Structure from motion (SFM), featured as lightweight and cheap [17,26], is a commonly used passive technology in phenotypic traits quantification [27,28], biomass estimation [29,30] and yield prediction [31]. A previous study achieved high precision for leaf area and volumetric shape of taproots of sugar beet in the laboratory [18].…”

Section: Introductionmentioning

confidence: 99%

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Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

et al. 2020

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Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.

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Section: Estimation Of Biomass Using Plant Phenotypic Traitsmentioning

confidence: 99%

Section: Estimation Of Biomass Using Plant Phenotypic Traitsmentioning

confidence: 99%

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Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

et al. 2020

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“…The numerous approaches for plant segmentation include the combination of colour information and 3D models [ADT11, MBW*18], adapted surface feature‐based techniques [PDMK13], model‐based approaches based on cylinder representations of stems and separate segmentation tailored to leaves [GDHB17], skeleton‐based stem and leaf point recognition approaches 2019, facet region growing approaches after an initial oversegmentation [LCT*18] as well as automated, data‐driven approaches for plant structure segmentation based on geometric features and Random Forests classifiers (e.g. [DNC*18]) or geometric features and clustering 2015.…”

Section: Related Workmentioning

confidence: 99%

Temporal Upsampling of Point Cloud Sequences by Optimal Transport for Plant Growth Visualization

Golla

Kneiphof

Kuhlmann

et al. 2020

Computer Graphics Forum

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Plant growth visualization from a series of 3D scanner measurements is a challenging task. Time intervals between successive measurements are typically too large to allow a smooth animation of the growth process. Therefore, obtaining a smooth animation of the plant growth process requires a temporal upsampling of the point cloud sequence in order to obtain approximations of the intermediate states between successive measurements. Additionally, there are suddenly arising structural changes due to the occurrence of new plant parts such as new branches or leaves. We present a novel method that addresses these challenges via semantic segmentation and the generation of a segment hierarchy per scan, the matching of the hierarchical representations of successive scans and the segment‐wise computation of optimal transport. The transport problems' solutions yield the information required for a realistic temporal upsampling, which is generated in real time. Thereby, our method does not require shape templates, good correspondences or huge databases of examples. Newly grown and decayed parts of the plant are detected as unmatched segments and are handled by identifying corresponding bifurcation points and introducing virtual segments in the previous, respectively successive time step. Our method allows the generation of realistic upsampled growth animations with moderate computational effort.

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“…However, in the case of complex and irregular objects made of partially overlapping items, like crop canopy structures, the quality of the 3D point cloud reconstruction is a relevant aspect to be taken into account (Liu et al 2018b). Several studies have confirmed the reliability and effectiveness of evaluating the crop phenotype from dense 3D point clouds (Mortensen et al 2018;Patrick and Li 2017;Lati et al 2013), leaves shape and distribution (Li and Tang 2017;Jay et al 2015), branches (Li and Tang 2017), and fruit zone architecture (Schöler and Steinhage 2015). The cited research works have not investigated only external crop features, like crop height and canopy volume, but also intra-canopy characteristics of the monitored crop.…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery

et al. 2019

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The Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopyatmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a costeffective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R 2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.

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Segmentation of lettuce in coloured 3D point clouds for fresh weight estimation

Cited by 50 publications

References 29 publications

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Temporal Upsampling of Point Cloud Sequences by Optimal Transport for Plant Growth Visualization

Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery

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