Plant leaf species identification using Curvelet transform

Prasad, Shitala; Kumar, Piyush; Tripathi, Rashmi

doi:10.1109/iccct.2011.6075212

Cited by 39 publications

(14 citation statements)

References 16 publications

(6 reference statements)

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“…The researchers have [31] employed curvelet based features [32], but there were some limitations such as distortions, that were introduced in the images due to a constant rescaling factor, which has not been normalized. Further, an automated leaf recognition system by using Multilayer Perceptron (MPL) [32] was developed and extracted 10 digital morphological features such as major axis length, minor axis length, eccentricity, orientation, convex area, filled area, equiv diameter, solidity, extent, and perimeter.…”

Section: Herbs Classification Based On Multiple Featuresmentioning

confidence: 99%

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

Muneer

Fati

2020

IEEE Access

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Recognizing the desired herb among thousands of herbs is an exhausting and time-consuming practice. Hence, herbs identification via a vision system is beneficial since the pharmacist and botanic need not to collect them through traditional ways. Thus, this paper proposed an efficient and automatic classification system to recognize Malaysian herbs that would be used in medical or cooking areas. As per the authors' knowledge, there is no evidence for similar studies on medical herbs in Malaysia. In the proposed system, we have investigated different classifiers to build an efficient classifier; then, the classifier was integrated with a mobile app to ease the real-time classification. The proposed system employed two classifiers, namely Support Vector Machine (SVM) and Deep Learning Neural Network (DLNN). The two models have been tested on our own dataset, which contains 1000 leaves. The experimental results showed that SVM achieved 74.63% recognition accuracy and DLNN achieved 93% recognition accuracy for both of the experimental model and the developed mobile app. Furthermore, the processing time was 4 seconds for SVM and 5 seconds for DLNN classifier, while the processing time using the mobile app was 2 seconds only.

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Section: Herbs Classification Based On Multiple Featuresmentioning

confidence: 99%

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

Muneer

Fati

2020

IEEE Access

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“…Studying the research papers of last few years, one may find curvelet transform technique that is used mostly for feature extraction [7]. If this data is analyzed properly and given to SVM based algorithm for training, then one can classify the type of leaf [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Infected Leaves and Botanical Diseases using Curvelet Transform

Tunio¹,

Memon²,

Khuhawar³

et al. 2019

ijacsa

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The study of plants is known as botany and for any botanist it is a daily routine work to examine various plants in their research lab. This research efforts an image processingbased algorithm for extracting the region of interest (ROI) from plant leaf in order to classify the specie and to recognize the particular botanical disease as well. Moreover, this paper addresses the implementation of curvelet transform on subdivided leaf images in order to compute the related information and train the support vector machine (SVM) classifier to execute better results. Furthermore, the paper presents a comparative analysis of existing and proposed algorithm for species and botanical diseases recognition over the dataset of leaves. The proposed multi-dimensional curvelet transform based algorithm provides relatively greater accuracy of 93.5% with leaves dataset.

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“…In 2011, an another procedure for extracting the features is presented by Prasad et al [4] for habitual plant recognition which will help the users or the research students for their researches on plant recognition. Techniques such as multidirectional Curvelet transform and multi-resolution is executed on semi-segmented leaf picture in-order to get hold of entire information of the picture.…”

Section: Literature Surveymentioning

confidence: 99%

Plant identification system using its leaf features

Pradeep¹,

Madhumathi²

2015

2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

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Plant identification based on leaf is becoming one of the most interesting and a popular trend. Each leaf carries unique information that can be used in the identification of plants. In the identification of plants based on leaf, the leaf images needs to be pre-processed accordingly to extract the various critical features. In this paper, we present the identification of plants based on leaf features using Multiclass SVM (MSVM) as a classifier. Index Terms-Morphological features, vein, texture features, Multiclass support vector machine (MSVM).

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Plant leaf species identification using Curvelet transform

Cited by 39 publications

References 16 publications

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

Efficient and Automated Herbs Classification Approach Based on Shape and Texture Features using Deep Learning

Detection of Infected Leaves and Botanical Diseases using Curvelet Transform

Plant identification system using its leaf features

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