Citation

Lee, Chee Cheong and Tan, See Yee and Lim, Tien Sze and Koo, Voon Chet (2019) Oil Palm Tree Detection from High Resolution Drone Image Using Convolutional Neural Network. Journal of Engineering Technology and Applied Physics, 1 (2). pp. 6-9. ISSN 26828383

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Abstract

We propose a method to combine several image processing methods with Convolutional Neural Network (CNN) to perform palm tree detection and counting. This paper focuses on drone imaging, which has a high image resolution and is widely deployed in the plantation industry. Analyzing drone images is challenging due to variable drone flying altitudes, resulting in inconsistent tree sizes in images captured. Counting by template matching or fixed sliding window size method often produces an inaccurate count. Instead, our method employs frequency domain analysis to estimate tree size before CNN. The method is evaluated using two images, ranging from a few thousand trees to a few hundred thousand trees per image. We have summarized the accuracy of the proposed method by comparing the results with manually labelled ground truth.

Item Type:	Article
Uncontrolled Keywords:	CNN, fourier analysis, palm tree detection, drone imaging
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	16 Jul 2024 01:45
Last Modified:	16 Jul 2024 01:45
URII:	http://shdl.mmu.edu.my/id/eprint/12620

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