In the coming decades, it is expected that mankind will need to double the quantity of food and biofuel produced in order to meet global demand [1
]. To achieve this with existing resources, new plant characteristics need to be identified, quantified, and bred to obtain more productive plant varieties within existing environments. This will require a greater understanding of how the genetic make-up of plants determines their phenotype (visible traits) in high resolution and in high throughput. Performing plant phenomics involves screening large germplasm collections to facilitate the discovery of new interesting traits (forward phenomics
), and analysing known phenotypic data in order to uncover the genes involved in their evolution and use these genes in plant breeding (reverse phenomics
]. Investigated plants are usually grown in thoroughly controlled conditions (growth chambers or glasshouses) and subjected to different environmental conditions and stresses (e.g. drought, salt, heat, etc.) with the primary aim of monitoring their phenotypic response using various measurements [2
Common plant morphological traits of interest include parameters such as main stem height, size and inclination, petiole length and initiation angle, and leaf width, length, inclination, thickness, area, and biomass [1
]. The usual procedure to collect these data consists of many laborious manual measurements, often requiring destructive harvests and thus multiple replicates of individual plant genotypes or varieties to allow successive harvests over time. A typical manual phenotypic analysis of 200 plants (daily objective) would require approximately 100 man-hours of work (
30 minutes per plant depending on the size and complexity), which is impractical. In light of the importance of gene discovery and agricultural crop improvement, the development of solutions to automate such a tedious task is imperative.
High-throughput plant phenotyping aims to extend the standard approach by growing, measuring and analysing temporally thousands of plants [5
]. In recent years, the plant phenotyping research has seen the emergence of high-throughput plant screening facilities [1
]; however, few image and mesh processing solutions are available to analyse the large amount of data captured and extract yield determinants (i.e. plant, leaf, or root characteristics). Among existing solutions, PHENOPSIS [7
] and GROWSCREEN [8
], provide 2D image-processing based semi-automated solutions for leaf phenotyping (leaf width, length, area, and perimeter) and root data monitoring (number of roots, root area, and growth rate). LAMINA [10
], another 2D-image based tool for leaf shape and size probing proposes a leaf analysis for various plant species. Recent image-processing solutions, such as TraitMill [11
] and HTPheno [12
], provide a more general plant analysis and measure information such as plant height, width, centre of gravity, projected area and bio-volume, and provide colorimetric analysis (e.g. greenness-differences between plants). Due to the importance of rice as a primary food resource, image-based solutions for rice phenotyping have been developed [6
] and involve the measurements of parameters such as grain size (length, width, and thickness), panicle length, and number of tillers. In the past 2 years, fully automated imaging techniques for the high-throughput investigation of plant root characteristics (yield determinants) have been developed [14
] to analyse non-destructively phenotypic traits such as root average radius, area, maximum horizontal width, and length distribution.
The latest applications have introduced a third dimension to the plant analysis. Stereo-imaging and mesh processing based systems, such as GROWSCREEN 3D [17
], the 3D imaging and RootReader3D software platform
], or the solution proposed in [19
], have pioneered the explicit 3D analysis of leaves and roots, allowing more accurate measurements of leaf area, and extraction of additional volumetric data.
To date, the literature is distinctly dominated by 2D image-processing techniques for high-throughput phenotyping of plants [6
]. The major limitation of these 2D solutions is the loss of crucial spatial and volumetric information (e.g. thickness, bending, rolling, orientation) when transposing available data from 3D to 2D. The recent introduction of new tools for plant analysis based on explicit 3D reconstructions [17
] (as opposed to inferred 3D based analysis [20
], widely used since the 1960’s) promises to increase potential of high-throughput studies in terms of accuracy and exhaustiveness of the measured features, but available three-dimensional solutions are currently focussed on a specific organ (e.g. leaves [17
] or roots [18
]), tailored to a particular image acquisition system [22
], and tend to be qualitative (or applied) rather than providing quantitative information and estimates of accuracy. Hence, a clear need exists for a more generalised plant analysis based on increasingly explicit 3D models and in which the reliability of the measurements is questioned and quantitatively assessed.
In this paper, we present a novel mesh-based technique developed for the high-throughput 3D analysis of plant aerial-parts. A focus is made on the feasibility of accurately extracting plant phenotypic parameters from a 3D mesh acquired for the dicotyledonous crop cotton. In this initial study, meshes were reconstructed using a low cost commercial 3D reconstruction system [23
]. The proposed methodology aims at a non-exhaustive, accurate, cross-sectional (observation of a representative subset of a population at a fixed time-point), and temporal investigation of the plant macroscopic phenotype. This requires advanced features such as plant mesh morphological segmentation [24
], accurate plant data extraction [26
], and plant organs tracking over-time. The mesh based methodology was tested on plant meshes reconstructed [23
] for a set of six plants studied at four time-points (i.e. 6×4 = 24 plant meshes).