<i>Phenomenal</i>: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

Artzet, Simon; Chen, Tsu‐Wei; Chopard, Jérôme; Brichet, Nicolas; Mielewczik, Michael; Cohen-Boulakia, Sarah; Cabrera‐Bosquet, Llorenç; Tardieu, François; Fournier, Christian; Pradal, Christophe

doi:10.1101/805739

Cited by 16 publications

(30 citation statements)

References 70 publications

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“…However, the underlying knowledge about genotypes and the differences in cultivation management have not been fully explored, indicating that high-throughput phenotypic acquisition is far from practical application. Therefore, it is urgent to establish automatic and online data analysis approaches [ 54 ]. However, owing to the complexity of plant morphological structure, it is difficult to realize automatic 3D segmentation from the plant morphological characteristics and regional growth method only.…”

Section: Discussionmentioning

confidence: 99%

Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

Teng

Wen

et al. 2021

GigaScience

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Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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Section: Discussionmentioning

confidence: 99%

Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

Teng

Wen

et al. 2021

GigaScience

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“…First, the model-free methods attempt to reconstruct a leaf surface from the 3D point cloud for a leaf in a bottom-up manner by relying on a leaf surface mesh representation, without requiring a prior mathematical model that describes the leaf. This category includes several methods, including the direct triangulation method that directly connects the points in the data to calculate the triangular mesh [ 15 – 17 ], Poisson surface reconstruction [ 18 – 21 ], nonuniform rational B-spline (NURBS) surface fitting [ 22 – 25 ], and locally weighted scatterplot smoothing (LOESS) [ 26 ]. These methods use local information from the point cloud to reconstruct the surface with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds

2021

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The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.

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“…Fruit appearance is a key trait for many crops and can condi-2 tion market viability of fruit products and the success of cul-successfully implemented to measure the shape and size of fruits such as strawberries (4, 12), apples (5), carrot (6, 14), mangoes (28), and many others. More recently, methodologies for 3D reconstruction of plant organs have been developed with approaches that vary in speed, scale, cost, and accuracy; including laser scanners, x-ray computed tomography, and reconstruction from sequences of 2D images from digital cameras (8, [29][30][31][32][33][34][35][36][37][38][39][40][41]. Methods that rely on sequences of 2D images are numerous and variable with their own complexities and nuances that provide different strengths and weaknesses (8,27,37,(40)(41)(42)(43)(44).…”

Section: Introductionmentioning

confidence: 99%

Cost-effective, high-throughput phenotyping system for 3D reconstruction of fruit form

Feldmann

Tabb

2021

Preprint

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Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi-view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer-grade components for capturing, calibrating, and reconstructing the 3D form of small-to-moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground-truth object and the reconstructed models was > 0.97 and root-mean square error (RMSE) was <3mm for objects without locally concave regions. Measurements from 1mm and 2mm resolution reconstructions were consistent (R2 > 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, 5 grapes, 7 peppers, and 4 Bosch and 2 red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.

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Phenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping

Cited by 16 publications

References 70 publications

Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds

Cost-effective, high-throughput phenotyping system for 3D reconstruction of fruit form

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