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Additional file 2. CNN for Spike Detection. Technical details of the overall Faster R-CNN architecture and step-wise description to train the model for spike detection.

Additional file 1. Image processing. Figure S1. Extracting the region of interest. First the rails of the wagon from the original image are detected using a combination of grayscale thresholding, Hough transforms and a number of morphological operations. The image is then ‘clipped’ to contain only the region of interest, hence removing the possibility of weeds or the colour checker being detected as foreground. Figure S2. Support Vector Machine. The images are segmented using the pictured support vector machine classifier which was trained on the output of the k-means clustering algorithm. The x and y axes are u and v values, respectively, from the Luv colour space. The gray and purple regions represent the green plant class and background class, respectively. The green and black circles represent training data of green plant pixel values and background pixel values, respectively.

Additional file 1. Image processing. Figure S1. Extracting the region of interest. First the rails of the wagon from the original image are detected using a combination of grayscale thresholding, Hough transforms and a number of morphological operations. The image is then â clippedâ to contain only the region of interest, hence removing the possibility of weeds or the colour checker being detected as foreground. Figure S2. Support Vector Machine. The images are segmented using the pictured support vector machine classifier which was trained on the output of the k-means clustering algorithm. The x and y axes are u and v values, respectively, from the Luv colour space. The gray and purple regions represent the green plant class and background class, respectively. The green and black circles represent training data of green plant pixel values and background pixel values, respectively.

Background 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. Results 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. Conclusions 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. Electronic supplementary material The online version of this article (10.1186/s13007-018-0308-5) contains supplementary material, which is available to authorized users.

In this article, we investigate the relationship between the range of optimal parameters of active contour models and the shape of the target object. We focus on the weights of the internal and external energy terms of the snakes functional. Our contributions are 3-fold. First, we propose a normalization step that brings the search space for optimal parameters into a bounded range. Secondly, we perform a systematic study of the behaviour of active contour models for all possible settings of their parameters and on a large set of synthetic geometric shapes. We introduce the concept of stability diagrams as a novel approach for assessing the stability of active contour models given a range of parameter values. Finally, we show that over a series of evolving shapes the region of the parameter domain that corresponds to suitable coefficients for segmentation, hereinafter referred to as feasible solution region, follows a predictable trend. Using shape diagrams as a metric for characterizing shapes quantitatively, we are able to correlate the shape of the objects to segment with the location and extent of the feasible solution region in the parameter domain. Refereed/Peer-reviewed

In this article we propose a novel tool that takes an initial segmented image and returns a more accurate segmentation that accurately captures sharp features such as leaf tips, twists and axils. Our algorithm utilizes basic a-priori information about the shape of plant leaves and local image orientations to fit active contour models to important plant features that have been missed during the initial segmentation. We compare the performance of our approach with three state-of-the-art segmentation techniques, using three error metrics.The results show that leaf tips are detected with roughly one half of the original error, segmentation accuracy is almost always improved and more than half of the leaf breakages are corrected. Refereed/Peer-reviewed

Thesis (PhD(Industry Doctoral Training Centre))--University of South Australia, 2016. Includes bibliographical references (pages 173-202) Plant phenotypic analysis, based on images captured as a function of plant variety, plant growth conditions and time, can offer useful information on the function of plant genes and the response of plants to environmental stresses. However, the majority of methods currently available to analyse these images are insufficient to fully utilise the large quantity of information available in the vast amount of images that can be captured. Correspondingly, the methods lag behind the ability of plant geneticists to generate novel varieties. Many image analysis methods are semi-automatic at best, with the accuracy of the segmentation step alone being an ongoing issue.

In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution―canopy shoot area as a function of height―which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an ᴿ² value of 0.92),and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopiesthan can be captured by a simple trait measure such as overall canopy height. Refereed/Peer-reviewed

The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90%accuracy. Refereed/Peer-reviewed

Modern genomics experiments are often conducted in controlled environments on hundreds of plants at a time. As such, manual and destructive phenotyping techniques are no longer suitable. Recently there has been a trend toward automated high-throughput phenotyping facilities, in an attempt to relieve the phenotyping bottleneck. A majority of these facilities focus on the use of cameras for non-destructive recording of plant data. Hence, the new challenge lies in accurately segmenting the plants from 2D images in an automated manner and then analysing structural and statistical information about them. In this paper we attempt to establish relationships between the parameter values of active contour models and the geometry of the objects/shapes that they are segmenting. Our goal is for users to be able to utilise some basic a-priori knowledge about the geometry of the object in order to automatically select a range of suitable parameter values. We analyse the accuracy of active contour models over multiple series of shapes that exhibit some pattern, such as decreasing number of sides or increasing concavity. We present a novel normalization technique so that the parameter values are of a similar scale. We also carefully design an experimental setup that ensures no bias between different shapes or parameter values. One such technique for image segmentation is known as the active contour model. Active contour models have seen widespread success throughout a number of applied fields due to their versatility and semiautomated nature. However, a high majority of these models rely on arbitrary parameters that are required to be selected manually. Furthermore, small variations in these parameters can produce substantial variations in the method's overall accuracy. This makes them unsuitable for use by non-experts and also for the analysis of a series of images that can change significantly over time. For example an image sequence for the growth of a plant. We show that over a series of shapes the range of parameters that provide convergence do follow a trend. We also show that not all contours that converge to the objects boundary do so in a stable manner, with a substantial amount oscillating continuously. However more information, such as more shapes and more parameter values, is required to draw meaningful and quantitative conclusions from such an analysis. Future work includes incorporating more of this information along with the application to more active contour models. Another exciting future direction is the use of 2D shape diagrams to quantify relationships between shapes, parameter values and levels of accuracy. Refereed/Peer-reviewed