基于卷积神经网络(CNN)和迁移学习的Hoya植物识别

Siti Kania Kushadiani, Budi Nugroho, S. Rahayu, G. Laxmi, Toto Haryanto
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引用次数: 0

摘要

印度尼西亚的Hoya植物物种多样性最高。由于花和叶的美丽,该属越来越受欢迎,爱好者的数量仍在增加。随着栽培过程中品种和栽培品种数量的增加,每个品种或栽培品种的鉴定对人们和爱好者来说都是一个问题。一个简单快速的识别系统是迫切需要的,尤其是嵌入在android或IOS系统中的应用。本研究旨在利用卷积神经网络(CNN)建立Hoya识别模型,使Hoya的确定更加容易。所得识别模型对Hoya的识别效果较好,准确率为90.476%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Hoya Plant using Convolutional Neural Network (CNN) and Transfer Learning
Indonesia has the highest species diversity of the Hoya plant. The genus is increasingly popular due to the beauty of the flowers and leaves, and the number of hobbyists is still increasing. As the number of species and cultivar is increasing during cultivation, the identification of each species or cultivar then become problematic for people and hobbyist. An easy and quick identification system is urgently needed, especially the application embedded in the android or IOS system. This study aimed to build a Hoya identification model using the Convolutional Neural Network (CNN) to make it easier for Hoya determination. The resulting identification model identified Hoya well, with an accuracy of 90.476%.
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