Abstract
Plant phenotyping is the quantitative description of a plant's physiological, biochemical, and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible, and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction.
Highlights
UNDERSTANDING the physiological and molecular mechanisms underlying the growth of plants and crops is increasingly important in order to reach food security goals whilst achieving sustainability within agricultural systems
40 images were taken for each of the three imaging methods whilst the removal in redundant images in the Active Vision Cell (AVC) led to a 10-48 percent reduction in image set
Static approaches often have cameras fixed in the environment making it extremely difficult and costly to adjust to different object sizes, which is crucial in plant phenotyping when trying to obtain growth information, or when creating a system capable of measuring multiple different species
Summary
UNDERSTANDING the physiological and molecular mechanisms underlying the growth of plants and crops is increasingly important in order to reach food security goals whilst achieving sustainability within agricultural systems. A comprehensive understanding of the possible range of plant phenotypes would aid breeding and genetic modification, facilitating the improvement e.g., of nutrient use and photosynthetic efficiency, thereby increasing crop yield and stability across diverse environments [3], [4]. Dimensions such as height and width, leaf area index (LAI), leaf area density (LAD) plus leaf angle and curvature are all important parameters that directly relate to the growth and light harvesting ability of plants.
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