Oil Palm Tree Detection from High Resolution Drone Image Using Convolutional Neural Network

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Published: Dec 17, 2019
DOI: https://doi.org/10.33093/jetap.2019.1.2.20
Keywords:
CNN, fourier analysis, palm tree detection, drone imaging

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Chee Cheong Lee
Faculty of Engineering and Technology, Multimedia University
See Yee Tan
Faculty of Engineering and Technology, Multimedia University
Tien Sze Lim
Faculty of Engineering and Technology, Multimedia University
Voon Chet Koo
Faculty of Engineering and Technology, Multimedia University

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.

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Vol. 1 No. 2 (2019): https://doi.org/10.33093/jetap.2019.1.2
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Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

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