ArticlePlant phenomics (Washington, D.C.)2024
Evaluating Neural Radiance Fields for 3D Plant Geometry Reconstruction in Field Conditions.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- BN-NeRF: A fast 3D reconstruction and phenotyping framework for banana plants using handheld devices.Plant phenomics (Washington, D.C.) · 2026Article
- A three-dimensional reconstruction method for seedlings based on improved DIFIX3D.Plant methods · 2026Article
- Fine-grained 3D rice phenotyping via multi-scale NeRF and multimodal segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- 2D-to-3D Image Reconstruction in Agriculture: A Review of Methods, Challenges, and AI-Driven Opportunities.Sensors (Basel, Switzerland) · 2026Review
- A novel point cloud completion model for three-dimensional reconstruction of complex, dynamic population-level crop canopy architecture.Plant communications · 2026Article
- From 3DGS scenes to plant traits: a scalable extraction and segmentation framework for muskmelon phenotyping.Frontiers in plant science · 2026Article
- A scalable framework for UAV-based point cloud reconstruction and organ segmentation of field-grown cotton in complex field environments.Frontiers in plant science · 2026Article
- Phenological growth stages of Finger millet (Frontiers in plant science · 2026Article
- A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond.Plant phenomics (Washington, D.C.) · 2025Review
- Deep learning in plant phenotyping: the first ten years.Plant phenomics (Washington, D.C.) · 2025Review
- TM-WSNet: A precise segmentation method for individual rubber trees based on UAV LiDAR point cloud.Plant phenomics (Washington, D.C.) · 2025Article
- A scalable and efficient UAV-based pipeline and deep learning framework for phenotyping sorghum panicle morphology from point clouds.Plant phenomics (Washington, D.C.) · 2025Article
- A Review of Optical-Based Three-Dimensional Reconstruction and Multi-Source Fusion for Plant Phenotyping.Sensors (Basel, Switzerland) · 2025Review
- A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs.Plants (Basel, Switzerland) · 2025Article
- High-fidelity wheat plant reconstruction using 3D Gaussian splatting and neural radiance fields.GigaScience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluate different Neural Radiance Field (NeRF) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in 3 scenarios with increasing complexity and compare the results with the point cloud obtained using light detection and ranging as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 min of training on the graphics processing unit, highlighting the efficacy of NeRFs for 3D reconstruction in challenging environments. Additionally, we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4% in the average F1 score. This optimization process substantially enhances the speed and efficiency of 3D reconstruction using NeRFs. Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.