文章基本信息

标题：Review on Techniques for Plant Leaf Classification and Recognition
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作者：Muhammad Azfar Firdaus Azlah ; Lee Suan Chua ; Fakhrul Razan Rahmad 等
期刊名称：Computers
电子版ISSN：2073-431X
出版年度：2019
卷号：8
期号：4
页码：77-98
DOI：10.3390/computers8040077
出版社：MDPI Publishing
摘要：Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.
关键词：leaf; pattern recognition; artificial neural network; probabilistic neural network; convolutional neural network; k-nearest neighbor; support vector machine leaf ; pattern recognition ; artificial neural network ; probabilistic neural network ; convolutional neural network ; k-nearest neighbor ; support vector machine