Leaves classification using neural network based on ensemble features

Sigit Adinugroho, Y. A. Sari
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引用次数: 10

Abstract

An automated plant identification is necessary to identify plants, especially rarely seen ones. In this paper a framework to identify plant species based on leaf's characteristics is introduced. First, 31 features of leaves from 13 species are extracted that represents color, shape and texture of the leaves. Then, the features are selected according to their correlation to the class label. The data with 25.8% pruned features are then used to train a feedforward neural network. The network is trained and tested using 975 images by implementing 10-fold mechanism yields 95.54% accuracy.
基于集成特征的神经网络叶片分类
自动植物识别是识别植物,特别是罕见植物的必要手段。本文介绍了一种基于叶片特征的植物物种识别框架。首先,提取13种植物叶片的31个特征,这些特征代表了叶片的颜色、形状和纹理。然后,根据特征与类标号的相关性选择特征。然后使用经过25.8%特征修剪的数据来训练前馈神经网络。通过实现10倍机制,使用975张图像对网络进行训练和测试,准确率为95.54%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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