Plants recognition using embedded Convolutional Neural Networks on Mobile devices

被引：1
|
作者：
Pechebovicz, Denise [1 ]
Premebida, Sthefanie [1 ]
Soares, Vinicios [1 ]
Camargo, Thiago [1 ]
Bittencourt, Jakson L. [1 ]
Baroncini, Virginia [1 ]
Martins, Marcella [1 ]
机构：
[1] Fed Univ Technol Parana Ponta Grossa UTFPR PG, Ponta Grossa, Parana, Brazil
来源：
2020 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) | 2020年
关键词：
Image Classification; Convolutional Neural Networks; Embedded Applications; Plant Recognition; MEDICINAL-PLANTS;
D O I：
10.1109/ICIT45562.2020.9067289
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this work we propose a mobile application capable of recognizing Brazilian medicinal plants to be used by universities, students that have not previous contact with the species and professionals working on health centers. We describe the database generation based on the Brazilian Ministry of Health list of medicinal and common toxic plants. We also implement artificial intelligence techniques to perform the recognition task using a class of convolutional neural networks (CNN) focused on lowering the computation resource necessary to run deep learning tasks and also optimizing the execution of the architectures on embedded and mobile devices.
引用
收藏
页码：674 / 679
页数：6
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