In-field Segmentation and Identification of Plant Structures using 3D Imaging

Abstract

Automatically correlating plant observable characteristics to their underlying genetics will streamline selection methods in plant breeding. Measurement of plant observable characteristics is called phenotyping, and knowing plant phenotypes accurately and throughout a plant’s growth is central to making breeding decisions. In-field plant phenotyping in an automated and noninvasive manner is hence crucial to accelerating plant breeding methods. However, most of the existing methods on plant phenotyping using visual imaging are confined to controlled greenhouse environments. This paper presents an automated method of mapping 2D images collected in an outdoor sorghum field to segmented 3D plant units that are of interest for phenotyping. This method leverages multiple horizontal and vertical viewpoints while capturing 2D images from a robotic platform so as to generate in-field 3D reconstructions of the sorghum plant. We develop and quantitatively evaluate segmentation methods on these 3D reconstructions and also compare against reconstructions obtained from a controlled greenhouse environment. We present analysis that contrasts the role of purely local geometric features and the effect of addition of global context in both datasets. This work furthers capabilities of in-field phenotyping which paves the way forward for plant biologists to study the coupled effect of genetics and environment on improving crop yields.