文章基本信息

标题：Detection of Infected Leaves and Botanical Diseases using Curvelet Transform
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作者：Nazish Tunio ; Abdul Latif Memon ; Faheem Yar Khuhawar 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：1
页码：516-520
DOI：10.14569/IJACSA.2019.0100166
出版社：Science and Information Society (SAI)
摘要：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 processing-based 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.
关键词：Region Of Interest (ROI); Support Vector Machine (SVM); feature extraction; curvelet transform; alternata; anthrac-nose; blightness