Review Article | Open Access

Volume 2026 |Article ID 100137 | https://doi.org/10.1016/j.plaphe.2025.100137

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

Jiajia Li,¹ Xinda Qi,¹ Seyed Hamidreza Nabaei,² Meiqi Liu,² Dong Chen,⁴ Qi Sun,⁵ Xin Zhang,^4,6 Xunyuan Yin,⁷ and Zhaojian Li ⁸

¹Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA
²Department of Systems and Information Engineering & Link Lab, University of Virginia, Charlottesville, VA, USA
³Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
⁴Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, USA
⁵Tandon School of Engineering, New York University, Brooklyn, USA
⁶School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, GA, USA
⁷School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore
⁸Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA

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.