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A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read NeRF and 3DGS push plant phenotyping past LiDAR

desk verdict Useful survey of 3D reconstruction for plant phenotyping with correct technical summaries, but the incomplete PRISMA appendix undercuts the claim of systematic coverage. read the letter →

arxiv 2505.00737 v1 pith:YALWLETJ submitted 2025-04-30 eess.IV cs.AIcs.CV

Jiajia Li , Xinda Qi , Seyed Hamidreza Nabaei , Meiqi Liu , Dong Chen , Xin Zhang , Xunyuan Yin , Zhaojian Li This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords Plantphenotyping3DreconstructionPointcloudNeuralradiancefieldsGaussiansplattingDeeplearningPrecisionagricultureHigh-throughput
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey sets out to organize the fast-moving field of 3D reconstruction for plant phenotyping around three families: classical depth-sensing and photogrammetric methods, neural radiance fields (NeRF), and 3D Gaussian splatting (3DGS). Its central claim is that the field is shifting from expensive, noisy, hard-to-scale classical pipelines toward deep-learning reconstructions that work from ordinary multi-view images, with 3DGS emerging as the most promising route because it combines explicit geometry with real-time rendering. The paper assembles representative studies across crops such as apple, rice, maize, cotton, and tomato, reports trait-estimation accuracies in terms of $R^2$, RMSE, MAPE, F1, and IoU, and argues that these methods can make automated high-throughput phenotyping practical. A sympathetic reader should come away with a clear map of which method to choose for which phenotyping task and where the remaining bottlenecks lie.

What carries the argument

The organizing axes of the review are two scene representations. NeRF models a scene as a continuous volumetric radiance field, an MLP $F_\theta(\mathbf{x},\mathbf{d})$ that maps each 3D location and viewing direction to a color and density $(\mathbf{c},\sigma)$, rendered by numerical integration of transmittance along rays. 3DGS replaces this implicit field with an explicit collection of learnable 3D Gaussians, each carrying a center $\boldsymbol{\mu}$, a covariance $\boldsymbol{\Sigma} = \boldsymbol{R}\boldsymbol{S}\boldsymbol{S}^T\boldsymbol{R}^T$, an opacity $\alpha$, and a color $\mathbf{c}$, rendered by projecting the ellipsoids to 2D and $\alpha$-blending them in depth order. This representational contrast drives the survey's central comparison: ray-marched implicit fields give photorealistic geometry at high training cost, while splatted explicit Gaussians give real-time rendering and faster optimization, which is what high-throughput phenotyping demands. The review also uses a tripartite metric scheme\textemdash pixel-level (PSNR, SSIM, LPIPS), geometry-level (IoU, Chamfer distance, boundary overlap, precision/recall/F1), and trait-level ($R^2$, RMSE, MAPE)\textemdash to compare studies that otherwise report incommensurable numbers.

What would settle it

Run the surveyed NeRF and 3DGS pipelines on a single standardized benchmark of, say, 100 field-grown maize plants with co-registered LiDAR ground truth; if 3DGS's trait MAPE exceeds 20% where the papers report 10\textendash 11%, or if NeRF's training time stays over an hour per plant at field scale, the survey's central claim that these methods make high-throughput phenotyping practical loses its empirical support.

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Extended reading notes

Core claim

The discovery the paper is trying to establish is that neural 3D reconstruction has reached the point where it can do what classical phenotyping hardware does, with cheaper and more flexible inputs. NeRF, trained only on multi-view RGB images and camera poses, reconstructs plant geometry that matches terrestrial laser scanning to sub-millimeter distances in greenhouse trials (0.865 mm mean error) and yields trait estimates with $R^2$ values from 0.89 to 0.98 for height, leaf area, stem thickness, and fruit volume. 3DGS goes further on the operational axis: because it represents geometry as explicit 3D Gaussians that are projected and $\alpha$-blended rather than ray-marched, it trains and renders far faster, and early plant studies report cotton boll count and stem length MAPEs around 10\textendash 11\% from smartphone-captured images. The survey's comparative conclusion is that 3DGS, not NeRF, is the paradigm most likely to scale to high-throughput field phenotyping, provided remaining issues of occlusion, dataset scarcity, and non-standard evaluation metrics are addressed.

Load-bearing premise

The survey's comparative conclusions stand on the accuracy and representativeness of the numbers it transcribes from the cited papers; if those reported results are misquoted, cherry-picked, or measured under incompatible protocols, the ranking of classical, NeRF, and 3DGS methods the survey presents would not hold.

Editorial extensions

If this is right

  • NeRF-based phenotyping can replace laser scanners for many indoor and greenhouse traits: a single RGB camera or smartphone, multi-view images, and pose estimation yield trait accuracies in the $R^2 \approx 0.89$\textendash $0.98$ range reported in the surveyed studies.
  • 3DGS brings real-time rendering and training fast enough for field-scale work, so the bottleneck shifts from reconstruction speed to data acquisition and segmentation quality rather than geometry computation.
  • Combining 3DGS with foundation segmentation models such as SAM enables organ-level trait extraction (boll count, stem length, leaf area) directly from Gaussian primitives, as the cotton and PlantGaussian studies demonstrate.
  • Multi-modal and hyperspectral extensions of NeRF/3DGS, the survey's main future direction, would let phenotyping measure functional traits like chlorophyll and stress alongside structure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A concrete economic consequence the paper leaves implicit: if 3DGS trait errors stay near the reported 10\% MAPE, a smartphone-video pipeline could replace LiDAR for row-crop phenotyping, reducing sensor cost by roughly two orders of magnitude and making per-plant trait measurement feasible at breeding-program scale.
  • Because the surveyed NeRF/3DGS plant studies mostly adapt generic models (Instant-NGP, Nerfacto, vanilla 3DGS) with no plant-specific loss terms, a testable extension is that crop-aware priors\textemdash for example, enforcing botanical consistency of leaf skeletons or stem connectivity\textemdash would close much of the remaining accuracy gap more cheaply than adding sensors.
  • The survey's call for standardized metrics implies a concrete benchmark design: fixed camera trajectories over a multi-species set with co-registered LiDAR ground truth, reported as $R^2$, MAPE, and Chamfer distance simultaneously; without it, the field's reported numbers will remain incommensurable and the comparative ranking unverifiable.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript presents a survey of 3D reconstruction techniques applied to plant phenotyping, with the stated aim of covering classical active and passive methods, Neural Radiance Fields (NeRF), and 3D Gaussian Splatting (3DGS). It provides mathematical preliminaries for NeRF and 3DGS, a taxonomy of evaluation metrics (pixel-level, geometry-level, trait-specific), and a table of recent representative studies. The paper claims to be a systematic review following PRISMA guidelines and includes a GitHub repository link for additional resources. The central assertion is that NeRF and 3DGS, especially 3DGS, offer efficiency and scalability advantages for automated high-throughput phenotyping, while classical methods face issues of noise, data density, and scalability.

Significance. If the methodological gaps are fixed, this survey would be a timely and useful reference for agricultural and computer-vision researchers entering the area. The descriptions of NeRF and 3DGS pipelines (Equations (1)-(11)) are standard and essentially correct, the metric taxonomy in Table 3 is well organized, and Table 4 compiles recent applications across crops. The paper also gives explicit credit to the emergence of datasets such as PlantGaussian and Splanting. Its comparative conclusions are qualitative rather than quantitative, but that is acceptable for a survey; the main risk to the paper's value is the incompleteness of the reported search methodology, which currently prevents verification of the 'systematic' claim.

major comments (3)
  1. [Appendix, page 14] The sentence 'only xx highly representative papers on traditional methods are included' contains an unresolved 'xx' placeholder. Combined with the absence of a PRISMA flow diagram and phase-wise inclusion/exclusion counts, this undermines the paper's central claim of a systematic and comprehensive review. A reader cannot determine how many classical papers were considered or whether the selection in Table 4 is representative. Please replace the placeholder with the actual number and add a complete PRISMA-style record: search dates, database-specific query strings, numbers of records identified, screened, excluded, and included.
  2. [Appendix, page 14] The statement '4 studies on NeRF (9 papers) and 3GS (3 Papers) in plant phenotyping were selected' is internally confusing. It does not state whether '4 studies' is the total number of included studies or only the NeRF studies, and it does not explain how 4 relates to the 9 NeRF papers and 3 3DGS papers. Please provide clear, non-ambiguous counts of included studies per method category and a grand total.
  3. [Section 3.1, page 7] The text states that classical methods are 'as summarized in Table 2' and refers readers to Table 2 for a more comprehensive review, but Table 2 is a summary of existing literature reviews, not a summary of classical reconstruction methods. This cross-reference mismatch weakens the survey's coverage of classical methods and would mislead readers. Either rename and restructure Table 2 to actually summarize classical method categories (with representative references) or correct the textual cross-reference.
minor comments (5)
  1. [Nomenclature, page 2] The entry 'PatchMatch Multi-View Stere' is missing a final 'o'; it should read 'PatchMatch Multi-View Stereo'.
  2. [Appendix, page 14] The abbreviation '3GS' is used inconsistently; the paper elsewhere uses '3DGS'. Please standardize to '3DGS' throughout, including the Appendix.
  3. [Section 3.3, pages 11-12] The text refers to the framework 'Splants' in Ojo et al. (2024), but the reference list correctly gives the title 'Splanting: 3D Plant Capture with Gaussian Splatting'. Please clarify which term denotes the framework and which denotes the dataset to avoid confusion.
  4. [Section 2.2, Equation (3)] The integration variable in Equation (3) is mismatched: the integral is written with respect to 'du' but the integrand is a function of 's'. It should be 'ds' for consistency with the notation used elsewhere in the paper.
  5. [Section 2.4.3, RMSE definition] The prose describing RMSE says it quantifies 'the average squared difference between predicted and actual values', but the formula correctly gives the square root of that average. Please align the wording with the formula.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a descriptive survey with no derivation whose conclusions reduce to its inputs; the self-citations are not load-bearing.

full rationale

This is a survey, not a derivation, so there is no prediction chain whose output is equivalent to its input. The paper's claims about classical methods, NeRF, and 3DGS are descriptive summaries of externally reported results, such as the R2, MAPE, F1, and IoU values in Section 3 and Table 4, and its comparative statements about efficiency and scalability are qualitative assessments attributed to the cited primary studies. Section 2 restates standard NeRF and 3DGS formulations from foundational sources rather than introducing a new derivation, and no fitted parameter is renamed as a prediction. The one identifiable self-citation, Li et al. (2023) in the Appendix regarding inclusion and exclusion standards, is used only as methodological background and does not carry the survey's central comparative conclusions, so it is not load-bearing. The Appendix's incomplete PRISMA reporting, including the placeholder 'only xx highly representative papers on traditional methods are included,' is a reproducibility and completeness limitation that weakens the claim of systematic comprehensiveness, but it is not a circularity because it does not make any conclusion true by definition or by fitting. Accordingly, no circular steps are identified and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

As a review paper, this work introduces no new free parameters, axioms, or invented entities. Its content is a synthesis of published methods and results from the cited literature.

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Cite this review

Pith. "Pith review of A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond." pith.science (2026). https://pith.science/paper/YALWLETJ

@misc{pith2026250500737,
  author       = {Pith},
  title        = {Pith review of: A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YALWLETJ}},
  note         = {Machine review of arXiv:2505.00737}
}
read the original abstract

Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment, making it crucial for advancing precision agriculture and crop improvement. 3D reconstruction technologies have emerged as powerful tools for capturing detailed plant morphology and structure, offering significant potential for accurate and automated phenotyping. This paper provides a comprehensive review of the 3D reconstruction techniques for plant phenotyping, covering classical reconstruction methods, emerging Neural Radiance Fields (NeRF), and the novel 3D Gaussian Splatting (3DGS) approach. Classical methods, which often rely on high-resolution sensors, are widely adopted due to their simplicity and flexibility in representing plant structures. However, they face challenges such as data density, noise, and scalability. NeRF, a recent advancement, enables high-quality, photorealistic 3D reconstructions from sparse viewpoints, but its computational cost and applicability in outdoor environments remain areas of active research. The emerging 3DGS technique introduces a new paradigm in reconstructing plant structures by representing geometry through Gaussian primitives, offering potential benefits in both efficiency and scalability. We review the methodologies, applications, and performance of these approaches in plant phenotyping and discuss their respective strengths, limitations, and future prospects (https://github.com/JiajiaLi04/3D-Reconstruction-Plants). Through this review, we aim to provide insights into how these diverse 3D reconstruction techniques can be effectively leveraged for automated and high-throughput plant phenotyping, contributing to the next generation of agricultural technology.

Figures

Figures reproduced from arXiv: 2505.00737 by the authors.

Figure 1
Figure 1. Comparison of different 3D reconstruction techniques and their corresponding reconstruction processes: (a) LiDAR, (b) Structured Light, (c) Structure from Motion (SfM), (d) Neural Radiance Fields (NeRF), and (e) 3D Gaussian Splatting (3DGS). The input to the network is the 3D location 𝒙 = (𝑥, 𝑦, 𝑧) and the 2D viewing direction (𝜃, 𝜙) of a sampling point along the camera ray, which can be computed from the known came… view at source ↗
Figure 2
Figure 2. Architecture of AgriNeRF and downstream fruit detection. Adapted from Chopra et al. (2024). Notably, researchers have extended NeRF’s applications to orchard environments, demonstrating its adaptability to complex agricultural settings. For example, in Hu et al. (2024), two state-of-the-art NeRF methods were explored for the 3D reconstruction of crop and plant models: Instant￾NGP (Müller et al., 2022), known for its… view at source ↗
Figure 3
Figure 3. Workflow comparison of NeRF-based and traditional 3D reconstruction methods for corn plants. Adapted from Arshad et al. (2024). of NeRF-based methods for 3D reconstruction in complex and challenging agricultural environments. The proposed framework is shown in [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Multi-view renderings of the NeRF model (a) Frontviews (b) Side views (c) Top views (d) Elevation views (Zhao et al., 2024). 3.3. 3DGS in plant phenotyping While NeRF-based methods have demonstrated success in 3D plant reconstruction, they often suffer from high comput…
Figure 5
Figure 5. Figure 5: Framework for 3D phenotyping for bell pepper using NeRF-based reconstruction and 3D scanning Zhao et al. (2024). The process integrates action camera and 3D scanner data, followed by NeRF reconstruction, scale restoration, segmentation, and phenotypic measurements. (a)…
Figure 6
Figure 6. Figure 6: Examples of 3D cotton bolls generated by Lidar and 3DGS (Jiang et al., 2025). limit their practical implementation in real-world agricul￾tural settings. First, NeRF models are computationally ex￾pensive, requiring long training times and significant GPU resources, maki…
Figure 7
Figure 7. Figure 7: The framework of the PlantGaussian model (Shen et al., 2025). The model processes multi-view videos to generate plant visualizations and surface outputs, starting with sparse reconstruction to derive sparse points and camera poses. Next, an improved Track Anything Mode…

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Cited by 1 Pith paper

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  1. IPENS:Interactive Unsupervised Framework for Rapid Plant Phenotyping Extraction via NeRF-SAM2 Fusion

    cs.CV 2025-05 conditional novelty 4.0 of 10

    IPENS lifts SAM2's 2D segmentations into 3D via NeRF mask inverse rendering, enabling interactive multi-target point cloud extraction and trait estimation for rice and wheat.

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.