Representing plant morphology is crucial for plant phenotyping, as it is a bottleneck in improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. However, capturing accurate 3D reconstructions of plants poses significant technical challenges. Nevertheless, recent advancements in implicit neural representations offer promising learning-based methods for reconstructing precise 3D representations of crops in the field. In this thesis, I provide a comprehensive review of 3D reconstruction techniques and their utility in creating photorealistic representations for 3D plant phenotyping. We propose a technique called ObjectCarver which decomposes a scene composed of multiple objects into the constituent geometries via separate signed distance fields. Lastly, I demonstrate the usage of signed distance functions in 3D plant phenotyping, extracting traits like leaf length, leaf width, and leaf area on three varietals of hemp plants.