Deep Learning-based Method for Multi-Class Classification of Oil Palm Planted Area on Plant Ages Using Ikonos Panchromatic Imagery

Soffiana Agustin, Handayani Tjandrasa, R.V. Hari Ginardi


Oil palm has many advantages, such as biofuels, cosmetics, food ingredients, etc. The amount of oil contained in oil palm fruit is very dependent on the age of the plant, so automatic detection of oil palm plantation area based on plant ages is required to estimate the amount of oil. The use of high-resolution satellite images in oil palm detection has shown promising results for small dimensions, and previous studies have used more than one band of the satellite images data. This will be a burden in terms of cost and processing. Previous studies regarding oil palm area detection usually focused on detecting land cover to distinguish oil palm and non-oil palm areas. This study proposes a method based on deep-learning convolutional neural networks to classify oil palm plantations at a productive age. The images used in this study are the Ikonos satellite image with panchromatic bands only, which have a spatial ratio of 1m. The plantation area is classified into the non-oil palm, oil palm areas with young, mature, and old ages. This study proposes a multi-class classification method for oil palm plantations based on plant ages using convolutional neural networks (CNN). This study performs two fine-tune models on a pre-trained CNN and then classified using SVM and CNN. The performance of CNN architectures such as AlexNet, VGG16, and VGG19 was compared. The highest accuracy is 94.74% when using the CNN classifier and fine-tune model-2 of the VGG19 pre-trained network.


multi-class classification; oil palm; plant ages; ikonos panchromatic images; fine-tune; convolutional neural network; support vector machine

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A. Alam, A. Er and H. Begum, "Malaysian oil palm industry: Prospect and problem," Journal of Food, Agriculture & Environment, vol. 13, no. 2, pp. 143-148, 2015.

D. Yuniardi, "Pusat Informasi Kelapa Sawit," PT. Fin Komodo Teknologi, 2015. [Online]. Available: [Accessed 03 April 2020].

"," Plantation Key Technology, 03 February 2018. [Online]. Available: [Accessed 03 April 2020].

A. Khamis, Z. Ismail, K. Haron and A. T. Mohammed, "Nonlinear Growth Models for Modeling Oil Palm Yield Growth," Journal of Mathematics and Statistics, vol. 1, no. 3, pp. 225-233, 2005.

A. Malangyudo, 19 April 2011. [Online]. Available: [Accessed 03 April 2020].

L. Li, J. Dong, S. N. Tenku and X. Xiao, "Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images," remote sensing, vol. 7, pp. 1206-1224, 2015.

N. I. Kwesi, Oil Palm Mapping Using Support Vector Machine With Landsat ETM+ Data, Enschede, Netherlands: University of Twente and Kwame Nkrumah University, 2012.

S. Daliman, S. A. Rahman, S. A. Bakar and I. Busu, "Segmentation of Oil Palm Area Based on GLCM-SVM and NDVI," IEEE Region 10 Symposium, pp. 645-651, 2014.

A. Chemura, Determining oil palm age from high resolution satellite imagery, Sweeden: Master of Science Thesis - University of Twente, Faculty of Geo-Information Science and Earth Observation, 2012.

H. M. Rizeei, H. Z. M. Shafri, M. A. Mohamoud, B. Pradhan and B. Kalantar, "Oil Palm Counting and Age Estimation from WorldView-3 Imagery and LiDAR Data Using an Integrated OBIA Height Model and Regression Analysis," Hindawi Journal of Sensors, 2018.

N. A. Mubin, E. Nadarajoo, H. Z. Mohd Shafri and A. Hamedianfar, "Young and mature oil palm tree detection and counting using convolutional neural network deep learning method," International Journal of Remote Sensing, vol. 40, no. 19, 2019.

D. Giardino, M. Matta, F. Silve, S. Spanò and V. Trobiani, “FPGA Implementation of Hand-written Number Recognition basedd on CNN,†International Journal on Advanced Science Engineering Information Technology, vol. Vol.9 , no. 1, pp. 167-171, 2019.

M. Freudenberg, N. Nolke, A. Agostini, K. Urban and F. Worgotter, "Large Scale Palm Tree Detection in High Resolution Satellite Images Using U-Net," remote sensing, vol. 11, no. 312, pp. 1-18, 2019.

W. Li, H. Fu, L. Yu and A. Cracknell, "Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images," Remote Sensing, vol. 9, no. 22, 2017.

W. Li, R. Dong, H. Fu and L. Yu, "Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images using Two-Stage Convolutional Neural Network," Remote Sensing, pp. 1-20, 2019.

R. C. Mat, A. R. M. Shariff, B. Pradhan, A. R. Mahmud and M. S. M. Rahim, "An effective visualization and comparison of online terrain draped with multi-sensor satellite images," Arabian Journal of Geosciences, vol. 6, p. 4881–4889, 2013.

S. Agustin, H. Ginardi and H. Tjandrasa, "Identification of Oil Palm Plantation in Ikonos Images using Radially Averaged Power Spectrum Values," in International Conference on Information, Communication Technology and System, Surabaya, Indonesia, 2015.

S. Agustin, P. A. R. Devi, D. Sutaji and N. Fahriani, "Oil Palm Age Classification on Satellite Imagery Using Fractal-Based Combination," Journal of Theoretical and Applied Information Technology, vol. 89, no. 1, pp. 18-27, 2016.

C. Rittgers, "Global Agricultural Information Network (GAIN)," 29 November 2019. [Online]. Available: [Accessed 26 February 2020].

K. Jusoff, "Mapping of Individual Oil Palm Trees Using Airborne Hyperspectral Sensing: An Overview," Applied Physics, vol. 1, no. 1, pp. 15 - 31, 2009.

X. Han, Y. Zhong, L. Cao and L. Zhang, "Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification," remote sensing, vol. 9, no. 848, pp. 1-22, 2017.

K. Simoyan and A. Zisserman, "Very Deep Convolutional Networks for Large Scale Image Recognition," in International Conference on Learning Representation (ICLR), New York, 2015.

D. Frossard, 17 June 2016. [Online]. Available: [Accessed 08 April 2020].



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