“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

Young, Therin J.; Jubery, Talukder Z.; Carley, Clayton N.; Carroll, Matthew E.; Sarkar, Soumik; Singh, Asheesh K.; Singh, Arti; Ganapathysubramanian, Baskar

doi:10.3389/fpls.2023.1141153

Cited by 6 publications

(5 citation statements)

References 102 publications

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“…After capturing the point cloud data, we cleaned, processed, and registered it using Faro SCENE 2021 ® software. Subsequently, our in-house Python script was utilized to extract individual plots from the registered point cloud data and remove the ground [43] (B) Rating: Following the removal of the ground portion, all plots were visualized using CloudCompare 2.11.3 ® ,and an expert assessed each plot's IDC stress on a scale of 1 to 5. (C) Visualization: Representative canopies for each IDC stress rating.…”

Section: Location and Field Scanningmentioning

confidence: 99%

“…With this more accurate ground definition, we fit a final plane through these refined ground points and keep everything above it as the true plant canopy. We utilized an in-house Python script for post-processing and plot extraction, as described in our previous study by Young et al [43]. Out of the 1100 canopies extracted, we selected 700 for downstream analysis.…”

Section: Plot Extraction and Idc Ratingmentioning

confidence: 99%

“…Canopy fingerprinting [43] is an approach for characterizing the three-dimensional structure of plant canopies. This approach involves analyzing 3D point cloud data of canopies to create detailed yet interpretable phenotypic traits.…”

Section: Canopy Fingerprinting For 3d Point Cloudsmentioning

confidence: 99%

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Soybean Canopy Stress Classification Using 3D Point Cloud Data

Young,

Chiranjeevi,

Elango

et al. 2024

Agronomy

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Automated canopy stress classification for field crops has traditionally relied on single-perspective, two-dimensional (2D) photographs, usually obtained through top-view imaging using unmanned aerial vehicles (UAVs). However, this approach may fail to capture the full extent of plant stress symptoms, which can manifest throughout the canopy. Recent advancements in LiDAR technologies have enabled the acquisition of high-resolution 3D point cloud data for the entire canopy, offering new possibilities for more accurate plant stress identification and rating. This study explores the potential of leveraging 3D point cloud data for improved plant stress assessment. We utilized a dataset of RGB 3D point clouds of 700 soybean plants from a diversity panel exposed to iron deficiency chlorosis (IDC) stress. From this unique set of 700 canopies exhibiting varying levels of IDC, we extracted several representations, including (a) handcrafted IDC symptom-specific features, (b) canopy fingerprints, and (c) latent feature-based features. Subsequently, we trained several classification models to predict plant stress severity using these representations. We exhaustively investigated several stress representations and model combinations for the 3-D data. We also compared the performance of these classification models against similar models that are only trained using the associated top-view 2D RGB image for each plant. Among the feature-model combinations tested, the 3D canopy fingerprint features trained with a support vector machine yielded the best performance, achieving higher classification accuracy than the best-performing model based on 2D data built using convolutional neural networks. Our findings demonstrate the utility of color canopy fingerprinting and underscore the importance of considering 3D data to assess plant stress in agricultural applications.

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Section: Location and Field Scanningmentioning

confidence: 99%

Section: Plot Extraction and Idc Ratingmentioning

confidence: 99%

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Soybean Canopy Stress Classification Using 3D Point Cloud Data

Young,

Chiranjeevi,

Elango

et al. 2024

Agronomy

Self Cite

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“…A digital representation of a plant’s structure in the form of a 3D model (point cloud or mesh) can be used to digitize morphological measurements down to the scale of individual organs [ 11 , 12 ]. Additionally, digital representations offer the potential to develop new 3D characteristics of plants that cannot be captured by humans like plant volume or surface area, establishing them as important traits for plant breeding [ 13 ]. However, this potential has not been extensively explored.…”

Section: Introductionmentioning

confidence: 99%

A 3D printed plant model for accurate and reliable 3D plant phenotyping

Bömer,

Esser,

Marks

et al. 2024

GigaScience

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Background This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool. Results Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from −10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community. Conclusion Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.

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“…Percentage of canopy cover: It was possible to derive information about the canopy, both from winter and summer clouds, using the cross-section tool in CC and extracting the contours of the canopy and soil as polylines [42,43]. The polylines were then exported as DXF files and imported into Autodesk AutoCAD (https://www.autodesk.it/products/autocad/overview?term = 1-YEAR&tab = subscription, accessed on 26 June 2023) for the calculation of the areas covered and uncovered by the canopy (Figure 7a).…”

mentioning

confidence: 99%

Low-Cost 3D Virtual and Dynamic Reconstruction Approach for Urban Forests: The Mesiano University Park

Chioni,

Maragno,

Pianegonda

et al. 2023

Sustainability

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Urban forests, parks, and gardens are fundamental components of urban sustainability, resilience, and regenerative dynamics. Designers, architects, and landscape architects could smartly manage these dynamic ecosystems if efficiently provided with design-oriented digital tools, technologies, and techniques. However, practitioners lack knowledge and standardized procedures for their uses. The rise of low-cost sensors to generate 3D data (e.g., point clouds) in forestry can also effectively support monitoring, analysis, and visualization purposes for greenery in urban contexts. Adopting an interdisciplinary approach—involving the fields of forestry, geomatics, and computer science—this contribution addresses these issues and proposes a low-cost workflow for 3D virtual reconstructions of urban forests to support information management activities and thus landscape architecture applications. By connecting a wide range of methods (i.e., spherical photogrammetry, point cloud modeling), tools (i.e., 360° camera, tablet with lidar sensor), and software (i.e., Agisoft Metashape, CloudCompare, Autodesk AutoCAD), the proposed workflow is defined and tested in the development of dynamic virtual representations for a plot of the Mesiano University park in Trento (Italy). Finally, comparing acquisition, processing, and elaboration methodologies and their results, the possibility of developing digital twins of urban forests is envisioned.

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“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

Cited by 6 publications

References 102 publications

Soybean Canopy Stress Classification Using 3D Point Cloud Data

Soybean Canopy Stress Classification Using 3D Point Cloud Data

A 3D printed plant model for accurate and reliable 3D plant phenotyping

Low-Cost 3D Virtual and Dynamic Reconstruction Approach for Urban Forests: The Mesiano University Park

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