One of the main challenges associated with image-based field phenotyping is the variability of illumination. During a single day’s imaging session, or between different sessions on different days, the sun moves in and out of cloud cover and has varying intensity. How is one to know from consecutive images alone if a plant has become darker over time, or if the weather conditions have simply changed from clear to overcast? This is a significant problem to address as colour is an important phenotypic trait that can be measured automatically from images. In this work we use an industry standard colour checker to balance the colour in images within and across every day of a field trial conducted over four months in 2016. By ensuring that the colour checker is present in every image we are afforded a ‘ground truth’ to correct for varying illumination conditions across images. We employ a least squares approach to fit a quadratic model for correcting RGB values of an image in such a way that the observed values of the colour checker tiles align with their true values after the transformation. The proposed method is successful in reducing the error between observed and reference colour chart values in all images. Furthermore, the standard deviation of mean canopy colour across multiple days is reduced significantly after colour correction is applied. Finally, we use a number of examples to demonstrate the usefulness of accurate colour measurements in recording phenotypic traits and analysing variation among varieties and treatments.

Segmentation of regions of interest is an important pre-processing step in many colour image analysis procedures. Similarly, segmentation of plant objects in digital images is an important preprocessing step for effective phenotyping by image analysis. In this paper, we present results of a statistical analysis to establish the respective abilities of different colour space representations to detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation for which the separation of the distributions representing object and background pixels is maximized is the best for the detection of plant pixels. The two pixel classes are modelled by Gaussian Mixture Models (GMMs). In our statistical modelling we make no prior assumptions on the number of Gaussians employed. Instead, a constant bandwidth mean-shift filter is used to cluster the data with the number of clusters, and hence the number of Gaussians, being automatically determined. We have analysed the following representative colour spaces: $RGB$ , $rgb$ , $HSV$ , $Ycbcr$ and $CIE$ - $Lab$ . We have analysed the colour space features from a two-class variance ratio perspective and compared the results of our model with this metric. The dataset for our empirical study consisted of 378 digital images (and their manual segmentations) of a variety of plant species: Arabidopsis, tobacco, wheat, and rye grass, imaged under different lighting conditions, in either indoor or outdoor environments, and with either controlled or uncontrolled backgrounds. We have found that the best segmentation of plants is found using $HSV$ colour space. This is supported by measures of Earth Mover Distance (EMD) of the GMM distributions of plant and background pixels.

This study describes a complete system for multiple‐target tracking in image sequences. The target appearance is represented as a set of weighted clusters in colour space. This is in contrast to the more typical use of colour histograms to model target appearance. The use of clusters allows a more flexible and accurate representation of the target, which demonstrates the benefits for tracking. However, it also introduces a number of computational difficulties, as calculating and matching cluster signatures are both computationally intensive tasks. To overcome this, the authors introduce a new formulation of incremental medoid‐shift clustering that operates faster than mean shift in multi‐target tracking scenarios. This matching scheme is integrated into a Bayesian tracking framework. Particle filters, a special case of Bayesian filters where the state variables are non‐linear and non‐Gaussian, are used in this study. An adaptive model update procedure is proposed for the cluster signature representation to handle target changes with time. The model update procedure is demonstrated to work successfully on a synthetic dataset and then on real datasets. Successful tracking results are shown on public datasets. Both qualitative and quantitative evaluations have been carried out to demonstrate the improved performance of the proposed multi‐target tracking system. A higher tracking accuracy in long image sequences has been achieved compared to other standard tracking methods.

In this study, the authors present an integrated approach for recovering both the intrinsic and extrinsic parameters of a camera from the silhouettes and feature point tracking of an object in a turntable image sequence. Their motivation in taking the integrated approach is to solve the problem of obtaining the Euclidean three‐dimensional (3D) reconstruction of cereal roots growing in gellan gum or objects immersed in water and imaged under turntable motion. The problem of self‐calibration by previous approaches is especially difficult in this case as the initialisation of the rotation axis from the bi‐tangent lines to the surface of revolution is not feasible. The conics projected from the circular trajectories of root tips are highly eccentric. Their approach is to initialise the axis of rotation l _s from the centre of the conics fitted to feature point trajectory. An estimate of l _s is then iteratively estimated by minimising an error function in estimating the harmonic homology introduced by the surface of symmetry. They compare the results of their approach to those of the maximum‐likelihood estimation‐based approach to conic fitting of point feature trajectories. They show results of real 3D reconstruction of roots, which are detailed enough for phenotypic analysis and are better both quantitatively and qualitatively than those using just feature point tracking.