Abstract
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.
Highlights
Automation is necessary in the agricultural industry to help accelerate the rate of increased crop productivity through genetic improvement techniques, in order to help cope with the rapid increase in human population and future demands on worldwide food security
Detecting nodes in a 3D point cloud is not a simple task; the following method is used to estimate the internode distances: leaf centers are projected onto the principal axis of the whole plant, the distance between these projected points is considered as the internode distance
These plant species were selected as examples of plants with: long leaves, leaves spreading vertically, very small leaves, curved leaves, long branches, overlapped leaves, leaves having natural texture (Figure 10j,k,p) and compound leaves with leaflets attached to a rachis
Summary
Automation is necessary in the agricultural industry to help accelerate the rate of increased crop productivity through genetic improvement techniques, in order to help cope with the rapid increase in human population and future demands on worldwide food security. Based on the plant meshes reconstructed using commercial software for 3D scanning [20], this study provided mesh segmentation, phenotypic parameter estimation and plant organ tracking over time to yield promising measurement accuracies of stem height and leaf size. This paper describes a novel 3D reconstruction system for plants that incorporates a number of unique hardware-based stereo features: multiple pairs of high-resolution color digital cameras, visible structured lights, ease of configuration adjustment and the ability to work indoors and outdoors. A complete hardware and software solution (including camera calibration, structured light control, stereo matching, the proposed 3D point cloud generation and registration, point cloud noise removal and segmentation and the proposed 3D leaf detection and 3D plant feature measurement) is created for both 3D reconstruction and non-destructive phenotyping measurement (plant height, number of leaves, leaf height and width and internode distances) of plants
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