Abstract
High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.
Highlights
The complex interaction between a genotype and its environment determines the observable phenotypic characteristics of a plant that influence resource acquisition and yield (Das Choudhury et al, 2019)
We present a novel method called 3DPhenoMV for computing 3D plant phenotypes based on a voxel-grid reconstruction approach using multiview visible light image sequences captured in an automated high throughput plant phenotyping platform (HTP3) where the distance between the pot and the camera is significantly larger (5.5 m) compared to the state-of-the methods. 3DPhenoMV uses a well-known space carving technique for voxel-grid reconstruction and aims to achieve the fully automatic reconstruction of a large number of plants at their late vegetative stages without requiring any manual intervention on an individual plant basis
While most state-of-the-art methods have addressed the 3D reconstruction of plants in early growth stages due to simplicity, we contribute in the research advancement of 3D phenotyping analysis of plants in the late vegetative stage by developing an algorithm, introducing a 3D plant phenotyping taxonomy and public release of a multiview benchmark dataset consisting of original image sequences of maize plants and images of checkerboard patterns for camera calibration
Summary
The complex interaction between a genotype and its environment determines the observable phenotypic characteristics of a plant that influence resource acquisition and yield (Das Choudhury et al, 2019). High throughput image-based plant phenotyping refers to the non-invasive monitoring and quantification of plants’ morphological and biophysical traits by analyzing their images captured at regular intervals with precision (Das Choudhury et al, 2018, 2019). It facilitates the analysis of a large number of plants in a relatively short time with no or little manual intervention to compute diverse phenotypes. Accurate estimation of the 3D structure of a plant to compute 3D phenotypes is important for the study of physiological processes in plants, e.g., plant leaf area and leaf angle significantly influence light interception, and thereby, transpiration, photosynthesis, and plant productivity (Thapa et al, 2018)
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