Identification of selected medicinal plant leaves using image features and ANN

R. Janani, A. Gopal
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引用次数: 60

Abstract

Identification of proper medicinal plants is quite challenging and it is the time to protect medicinal plants since several plant species are becoming extinct. Leaves are the key components of a plant. Here we have proposed a method for the extraction of shape, color and texture features from leaf images and training an artificial neural network (ANN) classifier to identify the exact leaf class. The key issue lies in the selection of proper image input features to attain high efficiency with less computational complexity. We tested the accuracy of the network with different combination of image features. The test results on 63 leaf images reveals that this method gives 94.4% accuracy with a minimum of eight input features. This approach is more promising for leaf identification systems that have minimum input and demand less computation time. This work has been implemented using the image processing and neural network toolboxes in MATLAB.
基于图像特征和人工神经网络的药用植物叶片识别
鉴定合适的药用植物是相当具有挑战性的,现在是时候保护药用植物,因为一些植物物种正在灭绝。叶子是植物的关键组成部分。本文提出了一种从叶子图像中提取形状、颜色和纹理特征的方法,并训练一个人工神经网络(ANN)分类器来识别准确的叶子类别。其关键问题在于选择合适的图像输入特征,以达到高效率和较低的计算复杂度。我们用不同的图像特征组合来测试网络的准确率。对63张树叶图像的测试结果表明,该方法在至少8个输入特征的情况下,准确率达到94.4%。对于输入最小、计算时间较短的叶片识别系统,该方法更有前景。本工作利用MATLAB中的图像处理和神经网络工具箱实现。
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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