Digital Morphometrics: A Tool for Leaf Morpho- Taxonomical Studies (original) (raw)

Plant species identification using Elliptic Fourier leaf shape analysis

Computers and Electronics in Agriculture, 2006

Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pigweed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pigweed, 93.6% of sunflower, 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping.

Leaf shape extraction for plant classification

2015 International Conference on Pervasive Computing (ICPC), 2015

The last few decades have witnessed various approaches to automate the process of plant classification using the characteristics of the leaf. Several approaches have been proposed, and the majority focused on global shape features. However, one challenge that faces this task is the high interclass similarity amongst the leaves of different species in terms of the global shape. Furthermore, there always has been an obstacle against full automation as several approaches require user intervention to align the leaf. Therefore, a new set of Quartile Features (QF) is proposed in this paper to describe the partial shape of the leaf, in addition to an automated alignment approach to automate the system. The QF are extracted from the horizontal and vertical leaf quartiles to describe the partial shape of the leaf and the relations among its parts. The well-known Flavia dataset has been selected for the evaluation of the proposed system. The experimental results indicate the ability of the proposed alignment algorithm to align leaves with different shapes and maintain a correct classification accuracy regardless of the orientation of the input leaf samples. Furthermore, the proposed QF indicated promising results by increasing the accuracy of the classification by a range of approximately 26% to 30% when combined with Hu's Moment Invariants, using k-fold cross-validation technique.

Exploring the Use of Leaf Shape Frequencies for Plant Classification

Fig. 1. Leaf shape modeling in the frequency domain. This figure shows the digital image process in combination with the Fourier descriptor for extracting a leaf shape signature. From left to right: original image; binary representation; extracted leaf contour; edge-centroid distance measure; normalised Fourier coefficients. Abstract-Plant identification and classification play an important role in ecology, but the manual process is cumbersome even for experimented taxonomists. Technological advances allows the development of strategies to make these tasks easily and faster. In this context, this paper describes a methodology for plant identification and classification based on leaf shapes, that explores the discriminative power of the contour-centroid distance in the Fourier frequency domain in which some invariance (e.g. rotation and scale) are guaranteed. In addition, it is also investigated the influence of feature selection techniques regarding classification accuracy. Our results show that by combining a set of features vectors-in the principal components space-and a feedforward neural network, an accuracy of 97.45% was achieved.

Plant species identification using digital morphometrics: A review

2012

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.

Evaluation of Features for Leaf Discrimination

2013

A number of shape features for automatic plant recognition based on digital image processing have been proposed by Pauwels et al. in 2009. A database with 15 classes and 171 leaf samples was considered for the evaluation of these measures using linear discriminant analysis and hierarchical clustering. The results obtained match the human visual shape perception with an overall accuracy of 87%.

Classification of Biological Species Based on Leaf Architecture–A review

extraction, 2012

Plants play an important role for the development of human society. The urgent situation is that due to environmental degradation, many rare plant species on the earth are still unknown and are at the margin of extinction, so it is necessary to keep record for plant protection. This research focuses on using digital image processing for the purpose of automate classification and recognition of plants based on the images of the leaves. The system consists of 4 main modules, 1) image acquisition, 2) image preprocessing, 3) image recognition and 4) display result. In the image acquisition module leaf image is captured by using digital camera. In the image preprocessing module, various image processing techniques are applied for preparing a leaf image for the features extraction process. In the image recognition module, various features are extracted from the leaf image and recognize it. In the display result module displays the recognition results. 12 kinds of leaves were taken to carry out the experiment. The accuracy of the system is 97.9 percent.

Classification of Leaf Using Geometric Features

2015

Every leaf has its own identity and pocess some information that will help humans to identify and classify the plant by analyzing its leaves. The shape of leaf is a significant feature that most people use to recognise and classify a plant. It uses different parameters like diameter, physiological length, physiological width, leaf area and perimeter are basic geometry information can be extract from the leaf shape. The leaf identification is essential for scientists working in the agricultural and environmental fields. This work is a study of leaf identification and recognition system.The steps include the capturing the leaf image followed by a preprocessing. Later discussing about different classifiers and their accuracies

LAMINA: a tool for rapid quantification of leaf size and shape parameters

BMC Plant Biology, 2008

Background: An increased understanding of leaf area development is important in a number of fields: in food and non-food crops, for example short rotation forestry as a biofuels feedstock, leaf area is intricately linked to biomass productivity; in paleontology leaf shape characteristics are used to reconstruct paleoclimate history. Such fields require measurement of large collections of leaves, with resulting conclusions being highly influenced by the accuracy of the phenotypic measurement process.

Extraction of Leaf Parts by Image Analysis

Lecture Notes in Computer Science, 2012

Leaf morphological characters are a useful visual guide for constructing relationships between different plants and between plants and their environment. However, extracting and analysing these characters are carried out manually by botanists, which is a painstaking and time-consuming task. One way to accelerate and broaden the use of these characters is to automatically extract them directly from images. An indispensable step toward this goal is to automatically detect leaf parts (petiole, blade, base, apex, rachis) since foliar characters are key descriptions about their shapes. In this paper we present a novel approach that addresses this problem. It is based on two types of symmetry: the first is local translational symmetry (for petiole, rachis detection). The second is local symmetry of depth indentations (for base and apex detection). The main advantage of this method is its accuracy and its robustness to shape variability. This is confirmed by the high rate of correct detections (more than 90%) obtained for a large number of leaf species.

Identification of Tropical Plants Leaves Image Base on Principal Component Analysis

Journal of Applied Agricultural Science and Technology

Difference and variation of leaves shape is usually used as primary identifier of the plant species. But some plants may have a similar leaf shape and thus require another more accurate identifier. This study applied principal component analysis (PCA) methods for identifying tropical plant species from the shape of the leaves. This method simplified the observed variables by reducing the dimensions of the information that is stored as much as 75%, so it did not eliminate important information and can save the data processing time. There were 100 images of leaves taken from several sides of the leaf in JPEG format with which the shape of leaves were look similar, like citrus (Citrus aurantifolia), durian (Durio zibethinus), guava (Psidium guajava), mango (Mangifera indica), jackfruit (Artocarpus heterophyllus), avocado (Persea americana), rambutan (Nephelium lappaceum), sapodilla (Manilkara zapota), red betel (Piper crocatum) and soursop (Annona muricata). Identification of those 10 ...