Leaf recognition based on artificial neural network

Furkan Ayaz, A. Ari, D. Hanbay
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引用次数: 3

Abstract

Plant recognition from their leaves has become a popular area in the machine learning and image processing. In this study 7 different types of apricot trees were determined and classified by using their leaves. At first leaves images were pre-processed. After than each image was scanned by 5×5 overlapping filter and median values of each filter process were recorded to represent the leaves. After than filtered each image was scanned by 2×2 overlapping filter and maximum values of each shifting step was recorded. The dimension of each image reduced to it' half. Histogram of these uniform patterns were evaluated. These features were applied as input to the Artificial Neural Network (ANN) and 7 types of apricot were classified with the accuracy is 98.6 %.
基于人工神经网络的叶片识别
植物叶片识别已成为机器学习和图像处理领域的一个热门领域。本研究利用杏叶对杏进行了7种不同类型的分类。首先,对树叶图像进行预处理。然后对每张图像进行5×5重叠滤波扫描,记录每个滤波过程的中值来代表叶片。滤波后的图像通过2×2重叠滤波器进行扫描,记录每个移动步长的最大值。每个图像的尺寸减少到原来的一半。对这些均匀模式的直方图进行评价。将这些特征作为输入输入到人工神经网络(ANN)中,对7种杏进行了分类,准确率达到98.6%。
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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