基于小波卷积神经网络的植物边缘和叶脉分类

Q3 Economics, Econometrics and Finance
Wulan Dewi, W. H. Utomo
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引用次数: 2

摘要

叶子是植物的一个器官,含有叶绿素,起到捕捉阳光能量的作用,阳光用于光合作用。完美的叶片由三部分组成,即中脉、茎和叶片。识别植物类型的方法是观察叶片边缘的形状。植物叶缘的形状、颜色和质地可能会影响其叶脉,在这种叶脉形态中,当形状、颜色或质地不明显时,叶脉形态携带着对植物分类有用的信息。另一方面,人类可能无法识别这一特征,因为他们更喜欢仅仅根据叶片形状而不是叶片边缘和叶脉来观察植物。本研究使用小波方法对数据集中现有的图像进行去噪,并使用卷积神经网络对图像进行分类。使用小波卷积神经网络方法获得的结果等于97.13%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK
The leaf is one of the plant organs, contains chlorophyll, and functions as a catcher of energy from sunlight which is used for photosynthesis. Perfect leaves are composed of three parts, namely midrib, stalk, and leaf blade. The way to identify the type of plant is to look at the shape of the leaf edges. The shape, color, and texture of a plant's leaf margins may influence its leaf veins, which in this vein morphology carry information useful for plant classification when shape, color, and texture are not noticeable. Humans, on the other hand, may fail to recognize this feature because they prefer to see plants solely based on leaf form rather than leaf margins and veins. This research uses the Wavelet method to denoise existing images in the dataset and the Convolutional Neural Network classifies through images. The results obtained using the Wavelet Convolutional Neural Network method are equal to 97.13%. 
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来源期刊
Applied Computer Science
Applied Computer Science Engineering-Industrial and Manufacturing Engineering
CiteScore
1.50
自引率
0.00%
发文量
0
审稿时长
8 weeks
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