You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
"Smartphone-Based High-Fidelity 3D Semantic Segmentation of Finger Mill" by Yican Yang, Mohan K. Bista et al.

Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.

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Smartphone-Based High-Fidelity 3D Semantic Segmentation of Finger Millet Panicles Using 3D Gaussian Splatting for Automated Phenotyping and Yield Estimation

Authors

Yican Yang
,
Mississippi State University

Mohan K. Bista
,
Mississippi State University

Alekhya Chakravaram
,
Mississippi State University

Nisarga Kodadinne Narayana
,
Mississippi State University

Raju Bheemanahalli
,
Mississippi State University

Lorin Harvey
,
Mississippi State University

Dong Chen
,
Mississippi State University

Nuwan K. Wijewardane
,
Mississippi State University

ORCID

Yang: https://orcid.org/0009-0005-4836-9389; Chakravaram: https://orcid.org/0009-0009-3311-5668; Wijewardane: https://orcid.org/0000-0001-8962-9451

MSU Affiliation

College of Agriculture and Life Sciences; Department of Plant and Soil Sciences; Department of Agricultural and Biological Engineering; James Worth Bagley College of Engineering

Creation Date

2026-06-30

Abstract

Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.

Keywords

finger millet, 3D reconstruction, semantic segmentation, neural radiance fields, 3D Gaussian splatting, Gaussian grouping

Publication Date

6-2-2026

Publication Title

Smart Agricultural Technology

Publisher

Elsevier

Creative Commons License

This work is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License

Rights

© 2026 The Author(s)

Recommended Citation

Yang, Y., Bista, M. K., Chakravaram, A., Narayana, N. K., Bheemanahalli, R., Harvey, L., Chen, D., & Wijewardane, N. K. (2026). Smartphone-based high-fidelity 3D semantic segmentation of finger millet panicles using 3D Gaussian splatting for automated phenotyping and yield estimation. Smart Agricultural Technology, 14, 102252. https://doi.org/10.1016/j.atech.2026.102252

Link to Full Text

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Digital Object Identifier (DOI)

https://doi.org/10.1016/j.atech.2026.102252

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</reference>

<statements>
1. Foundation-model-based pipelines integrate 3D scene priors with organ-level semantic segmentation, enabling organ separation, metric scale recovery, and trait extraction in seconds rather than minutes. These systems are particularly promising for high-throughput grain phenotyping, where thousands of seeds or panicles must be processed.
2. Grain-level phenotyping platforms, including robot-based seed imaging and NeRF/3DGS panicle pipelines, estimate seed or panicle length, width, thickness, volume, and morphotype classes, often achieving relative errors of a few percent compared to manual measurements.
3. Phenotypic traits \(y\) are functions of the reconstructed 3D structure. For instance, panicle volume can be computed from voxel grids or meshes, while grain number is obtained via clustering in point clouds.
4. Deep learning methods are increasingly used to accelerate reconstruction, reduce the required number of views, and automate segmentation.
5. Wheat3DGS and foundation-model pipelines use 3DGS plus multi-view segmentation to extract hundreds of wheat heads and measure length, width, and volume automatically.
6. Multi-view image sequences are captured by walking around a plant.
7. Camera poses are estimated via COLMAP or similar SfM tools.
8. NeRF or 3DGS-based reconstruction generates point clouds and semantic fields.
9. For an agricultural engineering researcher, adopting explicit state-space models, quality metrics, and optimal control/estimation strategies can transform ad hoc imaging setups into principled measurement systems with quantifiable performance guarantees and clear paths to scaling and field deployment.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.