Sinuosity Coefficients for Leaf Shape Characterisation

Kala, Jules Raymond; Viriri, Serestina; Moodley, Deshendran

doi:10.1007/978-3-319-27400-3_13

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Kala et al [10] use the border of leaf in a different way to other works. They compute a sinuosity measure, which expresses the meandering of a curve.…”

Section: Related Workmentioning

confidence: 99%

Leaf Categorization Methods for Plant Identification

López-Chau

Rojas-Hernández

García-Lamont

et al. 2017

Intelligent Computing Methodologies

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In most of classic plant identification methods a dichotomous or multi-access key is used to compare characteristics of leaves. Some questions about if the analyzed leaves are lobed, unlobed, simple or compound need to be answered to identify plants successfully. However, very little attention has been paid to make an automatic distinction of leaves using such features. In this paper we first explore if incorporating prior knowledge about leaves (categorizing between lobed simple leaves, and the unlobed simple ones) has an effect on the performance of six classification methods. According to the results of experiments with more than 1,900 images of leaves from Flavia data set, we found that it is statically significant the relationship between such categorization and the improvement of the performances of the classifiers tested. Therefore, we propose two novel methods to automatically differentiate between lobed simple leaves, and the unlobed simple ones. The proposals are invariant to rotation, and achieve correct prediction rates greater than 98%.

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“…Kala et al [10] use the border of leaf in a different way to other works. They compute a sinuosity measure, which expresses the meandering of a curve.…”

Section: Related Workmentioning

confidence: 99%

Leaf Categorization Methods for Plant Identification

López-Chau

Rojas-Hernández

García-Lamont

et al. 2017

Intelligent Computing Methodologies

View full text Add to dashboard Cite

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“…It has been extensively used in the domain of medical science for the analysis of the meandering of a spinal column (Tanguy and Peuchot, 2002) and in hydrography to evaluate the degree of meandering of a river (Langbein and Leopold, 1966). This article is an extension of a paper presented by Kala et al (2016), where the sinuosity coefficients were used to characterize leaf shapes. In this article, a leaf image is divided into four sections and the sinuosity coefficients are computed from each section to construct a model for leaf classification.…”

Section: Introductionmentioning

confidence: 99%

Plant Specie Classification Using Sinuosity Coefficients of Leaves

Kala

Viriri

2018

Image Anal Stereol

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Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition.

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Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition

Rajesh

2022

Research Anthology on Ecosystem Conservation and Preserving Biodiversity

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Plants are very important for the existence of human life. The total number of plant species is nearing 400 thousand as of date. With such a huge number of plant species, there is a need for intelligent systems for plant species recognition. The leaf is one of the most important and prominent parts of a plant and is available throughout the year. Leaf plays a major role in the identification of plants. Plant leaf recognition (PLR) is the process of automatically recognizing the plant species based on the image of the plant leaf. Many researchers have worked in this area of PLR using image processing, feature extraction, machine learning, and convolution neural network techniques. As a part of this chapter, the authors review several such latest methods of PLR and present the work done by various authors in the past five years in this area. The authors propose a generalized architecture for PLR based on this study and describe the major steps in PLR in detail. The authors then present a brief summary of the work that they are doing in this area of PLR for Ayurvedic plants.

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Sinuosity Coefficients for Leaf Shape Characterisation

Cited by 4 publications

References 7 publications

Leaf Categorization Methods for Plant Identification

Leaf Categorization Methods for Plant Identification

Plant Specie Classification Using Sinuosity Coefficients of Leaves

Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition

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