药用植物自动识别:基于多光谱和纹理特征的隐式深度学习模型

Murad Kabir Md. Rakib, Himanish Debnath Himu, Md. Omar Faruq Fahim, Ms. Zahura Zaman, MD. Jalal Uddin Rumi Palak
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引用次数: 0

摘要

在环境中自动识别药用植物对于了解它们在我们周围的存在是必要的。近年来,人们采用了许多自动识别植物的技术,如通过叶子和花朵的形状和纹理来识别植物。目前,基于叶片的植物物种识别系统得到了广泛的应用。本研究采用卷积神经网络(CNN)的深度学习方法,通过叶子对药用植物进行高准确率的识别。在本研究中,从自然界中收集树叶图像作为实验数据集。作者收集了5种不同药用植物的叶项。图像采集完成后,必须对其进行预处理,这在分类步骤中起着重要的作用。其中,使用深度学习模型和算法进行分类,VGG16表现较好,准确率达到95.48%。在现实生活中,这篇论文可以很好地影响医疗部门,了解更多的药用植物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Recognition of Medicinal Plants: Based on Multispectral and Texture Features using Hidden Deep Learning Model
Identification of medicinal plants automatically in the environments is necessary to know about their existence around us. Recently, there are many techniques followed to recognize plants automatically such as through leaves and flowers with their shape and texture. Leaf-based plant species identification systems are widely used nowadays. This proposed research work uses a deep learning approach using Convolutional Neural Networks (CNN) to recognize medicinal plants through leaves with high accuracy. For this research, leaf images are collected from nature and used as the experimental dataset. The authors have collected leaf items from 5 different medicinal plants. After the collection of images and have to pre-process them which plays an important role in the classification steps. Deep learning model and algorithm are used for classification purposes among them, VGG16 worked pretty well and got an accuracy level of 95.48%. In real life, this paper can well affect the medical sector and learn more about medicinal plants.
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