Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development

Aiman Yusoff, N. Kamarudin, Nabil Ali Al-Emad, Khusairi Sapuan
{"title":"Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development","authors":"Aiman Yusoff, N. Kamarudin, Nabil Ali Al-Emad, Khusairi Sapuan","doi":"10.46338/ijetae0223_02","DOIUrl":null,"url":null,"abstract":"— The difficulties to drive away the durian farm threatens animals such as wild boars, monkeys, foxes, and squirrels during nighttime often experienced by durian farmers. Therefore, the Pro Durian application is proposed that allows farmers to identify durian threats through a camera phone with an alert feature activation when the system detects an animal to drive away those animals. The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. The classification accuracies reached 80% in detecting the animal’s images. Keywords— Durian Farm, Recognition Image, TensorFlow lite, Android Studio, Convolution Neural Network","PeriodicalId":169403,"journal":{"name":"International Journal of Emerging Technology and Advanced Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Emerging Technology and Advanced Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46338/ijetae0223_02","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

— The difficulties to drive away the durian farm threatens animals such as wild boars, monkeys, foxes, and squirrels during nighttime often experienced by durian farmers. Therefore, the Pro Durian application is proposed that allows farmers to identify durian threats through a camera phone with an alert feature activation when the system detects an animal to drive away those animals. The application implements a deep learning algorithm of Convolutional Neural Network (CNN)-YOLO3in order to receive the best output results in identifying the different datasets of durian farm threats. The classification accuracies reached 80% in detecting the animal’s images. Keywords— Durian Farm, Recognition Image, TensorFlow lite, Android Studio, Convolution Neural Network
基于卷积神经网络和多媒体移动开发的榴莲农场威胁识别
——赶走榴莲农场的困难,威胁到了榴莲农民经常在夜间遇到的野猪、猴子、狐狸和松鼠等动物。因此,Pro榴莲应用程序被提出,允许农民通过带有警报功能的拍照手机识别榴莲威胁,当系统检测到动物时,警报功能会激活,并将这些动物赶走。该应用程序实现了卷积神经网络(CNN)- yolo3的深度学习算法,以便在识别榴莲农场威胁的不同数据集时获得最佳输出结果。在动物图像检测中,分类准确率达到80%。关键词:榴莲农场,识别图像,TensorFlow lite, Android Studio,卷积神经网络
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信