James Cope

Review: Plant species identification using digital morphometrics: A review

Plants are of fundamental importance to life on Earth. The shapes of leaves, petals and whole plants are of great significance to plant science, as they can help to distinguish between different species, to measure plant health, and even to model climate change. The growing interest in biodiversity and the increasing availability of digital images combine to make this topic timely. The global shortage of expert taxonomists further increases the demand for software tools that can recognize and characterize plants from images. A robust automated species identification system would allow people with only limited botanical training and expertise to carry out valuable field work. We review the main computational, morphometric and image processing methods that have been used in recent years to analyze images of plants, introducing readers to relevant botanical concepts along the way. We discuss the measurement of leaf outlines, flower shape, vein structures and leaf textures, and describe a wide range of analytical methods in use. We also discuss a number of systems that apply this research, including prototypes of hand-held digital field guides and various robotic systems used in agriculture. We conclude with a discussion of ongoing work and outstanding problems in the area.

Shape and Texture Based Plant Leaf Classification

This article presents a novel method for classification of plants using their leaves. Most plant species have unique leaves which differ from each other by characteristics such as the shape, colour, texture and the margin. The method introduced in this study proposes to use two of these features: the shape and the texture. The shape-based method will extract the contour signature from every leaf and then calculate the dissimilarities between them using the Jeffrey-divergence measure. The orientations of edge gradients will be used to analyse the macro-texture of the leaf. The results of these methods will then be combined using an incremental classification algorithm.

PLANT LEAF CLASSIFICATION USING PROBABILISTIC INTEGRATION OF SHAPE, TEXTURE AND MARGIN FEATURES

Plant species classification using leaf samples is a challenging and important problem to solve. This paper introduces a new data set of sixteen samples each of one-hundred plant species; and describes a method designed to work in conditions of small training set size and possibly incomplete extraction of features. This motivates a separate processing of three feature types: shape, texture, and margin; combined using a probabilistic framework. The texture and margin features use histogram accumulation, while a normalised description of contour is used for the shape. Two previously published methods are used to generate separate posterior probability vectors for each feature, using data associated with the k-Nearest Neighbour apparatus. The combined posterior estimates produce the final classification (where missing features could be omitted). We show that both density estimators achieved a 96% mean accuracy of classification when combining the three features in this way (training on 15 samples with unseen cross validation). In addition, the framework can provide an upper bound on the Bayes Risk of the classification problem, and thereby assess the accuracy of the density estimators. Lastly, the high performance of the method is demonstrated for small training set sizes: 91% accuracy is observed with only four training samples.

Plant texture classification using gabor co-occurrences

Leaves provide an important source of data for research in comparative plant biology. This paper presents a method for comparing and classifying plants based on leaf texture. Joint distributions for the responses from applying different scales of the Gabor filter are calculated. The difference between leaf textures is calculated by the Jeffreydivergence measure of corresponding distributions. This technique is also applied to the Brodatz texture database, to demonstrate its more general application, and comparison to the results from traditional texture analysis methods is given.

The Extraction of Venation from Leaf Images by Evolved Vein Classifiers and Ant Colony Algorithms

Leaf venation is an important source of data for research in comparative plant biology. This paper presents a method for evolving classifiers capable of extracting the venation from leaf images. Quantitative and qualitative analysis of the classifier produced is carried out. The results show that the method is capable of the extraction of near complete primary and secondary venations with relatively little noise. For comparison, a method using ant colony algorithms is also discussed.

Classifying plant leaves from their margins using dynamic time warping

Most plant species have unique leaves which differ from each other by characteristics such as the shape, colour, texture and the margin. Details of the leaf margin are an important feature in comparative plant biology, although they have largely overlooked in automated methods of classification. This paper presents a new method for classifying plants according to species, using only the leaf margins. This is achieved by utilizing the dynamic time warping (DTW) algorithm. A margin signature is extracted and the leafs insertion point and apex are located. Using these as start points, the signatures are then compared using a version of the DTW algorithm. A classification accuracy of over 90% is attained on a dataset of 100 different species.

A methodology to evaluate the effect of video compression on the performance of analytics systems

The Image Library for Intelligent Detection Systems (i-LIDS) provides benchmark surveillance datasets for analytics systems. This paper proposes a methodology to investigate the effect of compression and frame-rate reduction, and to recommend an appropriate suite of degraded datasets for public release. The library consists of six scenarios, including Sterile Zone (SZ) and Parked Vehicle (PV), which are investigated using two different compression algorithms (H.264 and JPEG) and a number of detection systems. PV has higher spatio-temporal complexity than the SZ. Compression performance is dependent on scene content hence PV will require larger bit-streams in comparison with SZ, for any given distortion rate. The study includes both industry standard algorithms (for transmission) and CCTV recorders (for storage). CCTV recorders generally use proprietary formats, which may significantly affect the visual information. Encoding standards such as H.264 and JPEG use the Discrete Cosine Transform (DCT) technique, which introduces blocking artefacts. The H.264 compression algorithm follows a hybrid predictive coding approach to achieve high compression gains, exploiting both spatial and temporal redundancy. The highly predictive approach of H.264 may introduce more artefacts resulting in a greater effect on the performance of analytics systems than JPEG. The paper describes the two main components of the proposed methodology to measure the effect of degradation on analytics performance. Firstly, the standard tests, using the ‘f-measure’ to evaluate the performance on a range of degraded video sets. Secondly, the characterisation of the datasets, using quantification of scene features, defined using image processing techniques. This characterization permits an analysis of the points of failure introduced by the video degradation.

Reverse engineering expert visual observations: From fixations to the learning of spatial filters with a neural-gas algorithm

Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.

Utilizing the hungarian algorithm for improved classification of high-dimension probability density functions in an image recognition problem

A method is presented for the classification of images described using high-dimensional probability density functions (pdfs). A pdf is described by a set of n points sampled from its distribution. These points represent feature vectors calculated from windows sampled from an image. A mapping is found, using the Hungarian algorithm, between the set of points describing a class, and the set for a pdf to be classified, such that the distance that points must be moved to change one set into the other is minimized. The method uses these mappings to create a classifier that can model the variation within each class. The method is applied to the problem of classifying plants based on images of their leaves, and is found to outperform several existing methods.

Machine Learning for Plant Leaf Analysis

One of the key challenges for automated analysis of plant leaves lies in the range of variation presented by a species and even by a single plant (Fig. 2.1). As well as the natural variation to be expected from any organic object, the variation of a leaf can arise from a number of sources, for example, its age and developmental stage. Shape varies during development, with early growth phases occurring primarily length-wise and increase in width coming later. In some species with lobed leaves, the leaf lobes are not apparent until after a certain stage in development. In others, like many Eucalyptus taxa, the leaves of young shoots are morphologically very distinct from those of mature ones. Margin characteristics such as teeth may not develop until the leaf has reached full size, often appearing first near the apex and then gradually extending further back towards the insertion point. Pigmentation often changes as the leaf develops.