A survey on 3D reconstruction techniques in plant phenotyping: From classical methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and beyond

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.

Plant phenotyping relevance

植物フェノタイピング向け3D再構成手法を体系的にレビューし、方法論・応用・性能を扱うことが中心であるため。

Code and data availability

This is a review article; the only author-provided public link (github.com/JiajiaLi04/3D-Reconstruction-Plants) is a companion repository for the survey, not a dataset, code, or model reproducing this paper's own plant-phenotyping measurements or computational analysis. Other URLs are the license and generic external (

No evidence-backed public reproduction asset is currently recorded.