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Author: search.filters.author.Antony Selvadoss ThanamaniHas files: Yes

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    FUZZY INFERENCE SYSTEM ALGORITHM OF PLANT CLASSIFICATION FOR TEA LEAF RECOGNITION
    (Indian Journal of Science and Technology, 2015) Arunpriya C; Antony Selvadoss Thanamani
    Background/Objectives: Biologists found that the morphological, physiological, bio-chemical and molecular methods of plant identification are found to be laborious and require great amount of technical knowledge. This research paper concentrates on the identification of varieties of tea using leaf images. It aims to identify the species in an easy and an accurate manner. Methods/Statistical analysis: The phases involved in this work are image pre processing, feature extraction and classification. Three classification algorithms such as Fuzzy Inference system, Radial basis function network and K-nearest neighbour were used and optimized to achieve a better accuracy and execution time. Results/Findings: The classification algorithm K-nearest neighbor, Radial basis function neural network and Fuzzy Inference System are trained with 40 samples and tested using 20 samples. Conclusions: Fuzz
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    A NEW FRAMEWORK FOR TEA PLANT RECOGNITION USING EXTREME LEARNING MACHINE WITH VERY FEW FEATURES
    (International Journal of Applied Engineering Research, 2015) Arunpriya C; Antony Selvadoss Thanamani
    Due to more and more tea varieties in the current tea market, rapid and accurate identification of tea varieties is crucial for tea quality control. Tea quality mainly depends on the variety of leaf, growing environment, manufacturing conditions, size of ground tea leaves and infusion preparation. In the past few years, tea cultivar has been assessed by morphological assessment coupled with pattern recognition. This paper uses an efficient machine learning approach called Extreme Learning Machine (ELM) for the classification purpose. The proposed approach consists of four phases which are as preprocessing, feature extraction, feature clustering and classification. Additionally, this work proposes an iterative algorithm for feature clustering and applies it to leaf recognition. Feature clustering is a powerful tool to reduce the dimensionality of the selected feature. For improving the accuracy and performance of tea leaf recognition, ELM is implemented. The classifier is tested with 20 leaves from each variety and compared with k-NN and RBF approach. The proposed ELM classification produces effective results.
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    AN EFFECTIVE TEA LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING IMPROVED ANFIS ALGORITHM
    (European Journal of Scientific Research, 2014) Arunpriya C; Antony Selvadoss Thanamani
    A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above the ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons). In this paper we have attempted to identify tea plant cultivars using classification techniques. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for efficient classification. The ANFIS is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in identifying the leaf type.
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    AN EFFECTIVE TEA LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING RADIAL BASIS FUNCTION MACHINE
    (International Journal of Modern Engineering Research, 2014-03) Arunpriya C; Antony Selvadoss Thanamani
    A leaf is an organ of a vascular plant, as identified in botanical terms, and in particular in plant morphology. Naturally a leaf is a thin, flattened organ bear above ground and it is mainly used for photosynthesis. Recognition of plants has become an active area of research as most of the plant species are at the risk of extinction. Most of the leaves cannot be recognized easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction and classification to process the loaded image. The tea leaf images can be identified accurately in the preprocessing phase by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT) in order to remove the noisy features and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) are derived to improve the classification accuracy. Radial Basis Function (RBF) is used for efficient classification. The RBF is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method classifies the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in retrieving the leaf type. Keywords
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    A NOVEL LEAF RECOGNITION TECHNIQUE FOR PLANT CLASSIFICATION
    (International Journal of Computer Engineering and Applications, 2014-02) Arunpriya C; Antony Selvadoss Thanamani
    Plants are the distinctive living things which incorporate many good things in it. At present due to environment degradation, many rare plant species on the earth are still unknown and are at the boundary of extinction. This must be avoided and they have to be preserved. This paper mainly focused on extraction of features for accurate classification of its types. Each plant leaves are different in shape, texture etc. By extracting the unique features in it they can be easily classified. Morphological and geometrical features from leaves are extracted here. Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for efficient classification. The ANFIS is trained by 50 different leaves to classify them into 5 types and its efficiency is calculated by accuracy and execution time factor.
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    A NEW LEAF ANALYSIS AND CLUSTERING FOR TEA SPECIES IDENTIFICATION
    (International Journal of Computer Engineering and Technology (IJCET), 2014-02) Arunpriya C; Antony Selvadoss Thanamani
    Leaf is an important organ of the plant. It is widely used for many purposes such as in medical field, chemical and other research purposes. Now it becomes active area for analysis of plants as most of the plant species are at the risk of extinction. Most of the leaves cannot be analyzed easily since some are not flat (e.g. succulent leaves and conifers), some does not grow above ground (e.g. bulb scales), and some does not undergo photosynthetic function (e.g. cataphylls, spines, and cotyledons).In this paper, we mainly focused on tea leaves to identify the leaf type for improving tea leaf classification. Tea leaf images are loaded from digital cameras or scanners in the system. This proposed approach consists of three phases such as preprocessing, feature extraction, selection and finally clustering of leaves. The tea leaf images are first preprocessed to remove the noise and enhanced by fuzzy denoising using Dual Tree Discrete Wavelet Transform (DT-DWT and boundary enhancement to obtain the shape of leaf accurately. In the feature extraction phase, Digital Morphological Features (DMFs) and Geometrical features are extracted and from that main features are selected. They are given to the clustering process which is done by using Fuzzy C-Means algorithm, it clearly cluster different type of tea leaves. The Fuzzy C-Means is trained by 60 tea leaves to classify them into 6 types. Experimental results proved that the proposed method clustered the tea leaves with more accuracy in less time. Thus, the proposed method achieves more accuracy in clustering the leaf type.
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    AN EFFICIENT LEAF RECOGNITION ALGORITHM FOR PLANT CLASSIFICATION USING KERNELIZED SUPPORT VECTOR MACHINE
    (International Journal of Computer Science and Management Research (IJCSMR), 2013-02) Arunpriya C; Antony Selvadoss Thanamani
    Plant recognition has become an active area of research as most of the plant species are at the risk of extinction. This paper uses an efficient machine learning approach for the classification purpose. This proposed approach consists of three phases such as preprocessing, feature extraction and classification. The preprocessing phase involves a typical image processing steps such as transforming to gray scale and boundary enhancement. The feature extraction phase derives the common DMF from five fundamental features. The values of the optimized σ are then used as a gauge for variable selection. In this study Kernelized (K-SVM) model is applied to several benchmark data sets in order to estimate the effectiveness of the second-order sigma tuning procedure for an RBF kernel.12 leaf features which are extracted and orthogonalized into 5 principal variables are given as input vector to the K-SVM. Classifier tested with flavia dataset and a real dataset and compared with k-NN approach, the proposed approach produces very high accuracy and takes very less execution time.
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    FUZZY SHRINK THRESHOLDING BASED TEA LEAF IMAGE ENHANCEMENT USING WAVELET TRANSFORM
    (International Journal of Computer Applications (IJCA), 2013-02) Arunpriya C; Antony Selvadoss Thanamani
    In this paper a wavelet shrinkage algorithm based on fuzzy logic is proposed to improve the tea leaf image. The Tea Leaf images are normally changes to unclear images by the presence of noise, low or high dissimilarity both in the edge area and also in the image area. The Fuzzy shrink is used to enhance the image. In exacting, intra-scale dependency within wavelet coefficients is modeled using a fuzzy characteristic. This characteristic space distinguishes between significant coefficients, which depends on image discontinuity and noisy coefficients. This fuzzy characteristic is used for enhancing wavelet coefficients' information in the shrinkage step in this paper. Then a fuzzy membership function known as the spline-based curve is used to shrinks the wavelet coefficients based on the fuzzy characteristic. Here by using the interrelation between different channels as a fuzzy characteristic for improving the denoising performance compared to denoising each channel, separately. Examine the image denoising algorithm in the dual-tree discrete wavelet transform, which is the latest shiftable and customized version of discrete wavelet transform. Extensive comparisons with the high-tech image denoising algorithm indicate that the image denoising algorithm has a better performance in noise suppression and edge preservation as compared with the other methods. The spline based curve of a fuzzy membership function is more efficient one
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    A SURVEY ON SPECIES RECOGNITION SYSTEM FOR PLANT CLASSIFICATION
    (International Journal of Computer Technology & Applications (IJCTA), 2012-05) Arunpriya C; Antony Selvadoss Thanamani
    Several attempts have been made by taxonomists and morphometricians to find out the best automated identification of biological species, but they haven’t found any effective species recognition system for several decades. It would be very helpful to carry out the behavioral and ecological studies on plants for plant classification with the help of species recognition system. Each species of a plant and its leaf has its own distinctive patterns, which enabled the researchers to perform some research on it to accurately classify the plants. In general, plant species recognition system includes image categorization and object recognition. Plant species recognition system is completely different from common image categorization, since the variation between the one plant species leaf and other is very small. As a result, the traditional image categorization techniques do not perform effectively on plant images. Automatic plant recognition system has not yet been well established largely being the fact that lack of research in this field and the complexity in obtaining the database. Species recognition system on plants is one of the major concerns at present and there is huge requirement for several researches to deal with the better plant species recognition system. In this survey, several plant species recognition system are discussed which will show the way for development of better plant species recognition system for plant classification.