6,516 research outputs found
Structured Light-Based 3D Reconstruction System for Plants.
Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance
3D Scanning System for Automatic High-Resolution Plant Phenotyping
Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing
structures make plants difficult for three-dimensional (3D) scanning and
reconstruction -- two critical steps in automated visual phenotyping. Many
current solutions such as laser scanning, structured light, and multiview
stereo can struggle to acquire usable 3D models because of limitations in
scanning resolution and calibration accuracy. In response, we have developed a
fast, low-cost, 3D scanning platform to image plants on a rotating stage with
two tilting DSLR cameras centred on the plant. This uses new methods of camera
calibration and background removal to achieve high-accuracy 3D reconstruction.
We assessed the system's accuracy using a 3D visual hull reconstruction
algorithm applied on 2 plastic models of dicotyledonous plants, 2 sorghum
plants and 2 wheat plants across different sets of tilt angles. Scan times
ranged from 3 minutes (to capture 72 images using 2 tilt angles), to 30 minutes
(to capture 360 images using 10 tilt angles). The leaf lengths, widths, areas
and perimeters of the plastic models were measured manually and compared to
measurements from the scanning system: results were within 3-4% of each other.
The 3D reconstructions obtained with the scanning system show excellent
geometric agreement with all six plant specimens, even plants with thin leaves
and fine stems.Comment: 8 papes, DICTA 201
Approaches to three-dimensional reconstruction of plant shoot topology and geometry
There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention – particularly from computer vision researchers – and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements
A model-based approach to recovering the structure of a plant from images
We present a method for recovering the structure of a plant directly from a
small set of widely-spaced images. Structure recovery is more complex than
shape estimation, but the resulting structure estimate is more closely related
to phenotype than is a 3D geometric model. The method we propose is applicable
to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of
thin elements with few identifiable features, making it difficult to analyse
using standard feature matching techniques. Our method instead analyses the
structure of plants using only their silhouettes. We employ a generate-and-test
method, using a database of manually modelled leaves and a model for their
composition to synthesise plausible plant structures which are evaluated
against the images. The method is capable of efficiently recovering accurate
estimates of plant structure in a wide variety of imaging scenarios, with no
manual intervention
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