Object-Centric 3D Gaussian Splatting for Strawberry Plant Reconstruction and Phenotyping

• URL: http://arxiv.org/abs/2511.02207v1
• Date: Tue, 04 Nov 2025 02:55:46 GMT
• Title: Object-Centric 3D Gaussian Splatting for Strawberry Plant Reconstruction and Phenotyping
• Authors: Jiajia Li, Keyi Zhu, Qianwen Zhang, Dong Chen, Qi Sun, Zhaojian Li,
• Abstract summary: Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales.<n>Traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive.<n>We propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline.
• Score: 21.752518657362888
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.

Related papers

• OracleGS: Grounding Generative Priors for Sparse-View Gaussian Splatting [78.70702961852119]
  OracleGS reconciles generative completeness with regressive fidelity for sparse view Gaussian Splatting.<n>Our approach conditions the powerful generative prior on multi-view geometric evidence, filtering hallucinatory artifacts while preserving plausible completions in under-constrained regions.
  arXiv  Detail & Related papers  (2025-09-27T11:19:32Z)
• HairGS: Hair Strand Reconstruction based on 3D Gaussian Splatting [50.93221272778306]
  Human hair reconstruction is a challenging problem in computer vision.<n>We extend the 3DGS framework to enable strand-level hair geometry reconstruction from multi-view images.<n>Our method robustly handles a wide range of hairstyles and achieves efficient reconstruction, typically completing within one hour.
  arXiv  Detail & Related papers  (2025-09-09T14:08:41Z)
• Three-dimentional reconstruction of complex, dynamic population canopy architecture for crops with a novel point cloud completion model: A case study in Brassica napus rapeseed [4.377318975816766]
  We propose an effective method for 3D reconstruction of complex, dynamic population canopy architecture for rapeseed crops.<n>A complete point cloud generation framework was developed for automated annotation of the training dataset.<n>The crop population point cloud completion network (CP-PCN) was then designed with a multi-resolution dynamic graph convolutional encoder (MRDG) and a point pyramid decoder (PPD) to predict occluded points.
  arXiv  Detail & Related papers  (2025-06-23T05:02:31Z)
• A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond [15.48852238904361]
  Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment.<n>3D reconstruction technologies have emerged as powerful tools for capturing detailed plant morphology and structure.<n>This paper provides a review of the 3D reconstruction techniques for plant phenotyping.
  arXiv  Detail & Related papers  (2025-04-30T02:04:23Z)
• CropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop Plants [16.558411700996746]
  We present a novel method for 3D reconstruction of agricultural crops based on optimizing a model of plant morphology via inverse procedural modeling. We validate our method on a dataset of real images of agricultural fields, and demonstrate that the reconstructions can be used for a variety of monitoring and simulation applications.
  arXiv  Detail & Related papers  (2024-11-14T18:58:02Z)
• BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions [28.79416825695514]
  Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability.<n> Advancements in field management through non-chemical weeding by robots in combination with monitoring of crops by autonomous unmanned aerial vehicles (UAVs) are helpful to address these challenges.<n>The analysis of plant traits, called phenotyping, is an essential activity in plant breeding, it however involves a great amount of manual labor.
  arXiv  Detail & Related papers  (2023-12-22T14:06:44Z)
• High-fidelity 3D Reconstruction of Plants using Neural Radiance Field [10.245620447865456]
  We present a novel plant dataset comprising real plant images from production environments. This dataset is a first-of-its-kind initiative aimed at comprehensively exploring the advantages and limitations of NeRF in agricultural contexts.
  arXiv  Detail & Related papers  (2023-11-07T17:31:27Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction [72.05649682685197]
  State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints. Motivated by recent advances in the area of monocular geometry prediction, we explore the utility these cues provide for improving neural implicit surface reconstruction.
  arXiv  Detail & Related papers  (2022-06-01T17:58:15Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.