A survey on 3D reconstruction techniques in plant phenotyping: from classical methods to neural radiance fields (NeRF), 3D Gaussian splatting (3DGS), and beyond

Plant phenotyping is essential for advancing precision agriculture and crop improvement, and 3D reconstruction technologies offer powerful tools for capturing detailed plant morphology. This paper investigate the latest advancements in 3D reconstruction for plant phenotyping, focusing on traditional point cloud methods, Neural Radiance Fields (NeRF), and 3D Gaussian Splatting (3DGS). While point clouds are widely used for their simplicity, they face challenges with data density and noise. NeRF provides photorealistic reconstructions from sparse views but is computationally intensive. The novel 3DGS approach offers efficient, scalable representations using Gaussian primitives. This paper evaluates these methods, highlighting their strengths, limitations, and potential in automated phenotyping.