叶缘特征结合形态变换和SIFT关键点的植物物种分类

Q3 Computer Science

中国图象图形学报 Pub Date : 2023-03-01 DOI:10.18178/joig.11.1.91-97

Jiraporn Thomkaew, Sarun Intakosum

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引用次数: 1

摘要

提出了一种将叶缘特征与形态学变换相结合，利用SIFT定义叶缘关键点的植物分类新方法。这个过程有三个步骤。图像预处理，特征提取，图像分类。在图像预处理步骤中，使用形态学变换去除图像噪声，使用Canny边缘检测检测叶子边缘。利用SIFT对叶片边缘进行识别，并根据该方法对植物叶片特征进行CNN提取。然后用随机森林法对植物叶片进行分类。实验在PlantVillage的10类、5类健康叶片和5类患病叶片数据集上进行。结果表明，该方法能够比基于叶片形状和纹理的特征更准确地分类植物物种。该方法的准确率为95.62%。

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Plant Species Classification Using Leaf Edge Feature Combination with Morphological Transformations and SIFT Key Point

This paper presents a new approach to plant classification by using leaf edge feature combination with Morphological Transformations and defining key points on leaf edge with SIFT. There are three steps in the process. Image preprocessing, feature extraction, and image classification. In the image preprocessing step, image noise is removed with Morphological Transformations and leaf edge detect with Canny Edge Detection. The leaf edge is identified with SIFT, and the plant leaf feature was extracted by CNN according to the proposed method. The plant leaves are then classified by random forest. Experiments were performed on the PlantVillage dataset of 10 classes, 5 classes of healthy leaves, and 5 classes of diseased leaves. The results showed that the proposed method was able to classify plant species more accurately than using features based on leaf shape and texture. The proposed method has an accuracy of 95.62%.

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来源期刊

中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6776

期刊介绍： Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.