UMP Institutional Repository

Oil palm tree detection and counting in aerial images based on faster R-CNN

Xinni, Liu and Kamarul Hawari, Ghazali and Fengrong, Han and Izzeldin, I. Mohd and Yue, Zhao and Yuanfa, Ji (2020) Oil palm tree detection and counting in aerial images based on faster R-CNN. In: InECCE2019: Proceedings of the 5th International Conference on Electrical, Control & Computer Engineering, 29th July 2019 , Kuantan, Pahang, Malaysia. pp. 475-482., 632. ISSN 1876-1100 ISBN 9789811523168

Oil palm tree detection and counting in aerial images .pdf

Download (161kB) | Preview


Malaysian oil palm industry has been a great contributor to the country’s creation of job opportunity, foreign exchange earnings and GDP. Information about the amount and the distribution of oil palm trees in a plantation are important for sustainable management. In this paper, we propose an oil palm tree detection and counting method based on the Faster Regions with Convolutional Neural Network algorithm (Faster R-CNN). Experiment on the oil palm tree images collected by a drone shows that the proposed method can effectively detect the oil palm trees and counting its number when the age of the trees in a plantation is different from 2 years old to 8 years old. The proposed approach can be used to predict the scale of the plantation and meets the requirements of real-time detection.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Lecture Notes in Electrical Engineering
Uncontrolled Keywords: Object detection; Oil palm tree; Convolution neural network; Drone imagery
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
College of Engineering
Faculty of Electrical and Electronic Engineering Technology
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 28 Dec 2020 05:54
Last Modified: 06 Jan 2021 03:51
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item