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

Aiman Yusoff, Aiman Yusoff and Noraziahtulhidayu Kamarudin, Noraziahtulhidayu Kamarudin and Nabil Ali Al-Emad, Nabil Ali Al-Emad and Khusairi Sapuan, Khusairi Sapuan (2023) Durian Farm Threats Identification through Convolution Neural Networks and Multimedia Mobile Development. International Journal of Emerging Technology and Advanced Engineering, 13 (2). pp. 8-15. ISSN 2250-2459

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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.

Item Type:	Article
Uncontrolled Keywords:	Durian Farm, Recognition Image, TensorFlow lite, Android Studio, Convolution Neural Network
Subjects:	T Technology > TJ Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Divisions:	Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User:	Mr. Mohamad Zulkhibri Rahmad
Date Deposited:	25 Oct 2023 07:27
Last Modified:	25 Oct 2023 07:27
URI:	http://eprints.uthm.edu.my/id/eprint/10251

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