FPGA-Based Plant Identification Through Leaf Veins

被引：7

作者：

Linsangan, Noel B. ^{[1
]}

Pangantihon, Rodrigo S., Jr. ^{[2
]}

机构：

[1] Mapua Univ, SEECE, Manila, Philippines

[2] Univ Mindanao, Coll Engn Educ, Comp Engn Program, Davao, Philippines

来源：

2018 5TH INTERNATIONAL CONFERENCE ON BIOMEDICAL AND BIOINFORMATICS ENGINEERING (ICBBE 2018) | 2018年

关键词：

Cyclone V; convolutional neural network; plant identification; FPGA; OpenCL;

D O I：

10.1145/3301879.3301905

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Identifying and understanding numerous plant species is of utmost importance not only to botanists but also to non-professionals so that they will be able to recognize its name and characteristics. This study is focusing on plant identification with the use of field-programmable gate array (FPGA) device Altera DE1-SoC Cyclone V platform. An openly accessible high-level synthesis (HLS) tool was used to perform image classification of plants leaf using the convolutional neural network (CNN) based on the technology called OpenCL. A total of fifty plants with five leaf samples per plant were being trained using Caffe. The system has yielded an accuracy rate of 88.68% with the aid of a confusion matrix tool.

引用

页码：100 / 104

页数：5

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