Identifikasi Tingkat Kematangan Kelapa Sawit Berbasis Pencitraan Termal

Khusnul Azima, Khairul Munadi, Fitri Arnia, Maulisa Oktiana


Indonesia is the biggest producer of palm oil (Elaeis guineenis jacq).  The palm tree is a primary commodity that posses a high economic value. Palm oil must be considered in terms of quality to produce optimal and high-quality oil. Previously, the stipulation of the palm tree characterization used manual and visual image utilization method; it may have weaknesses due to the dependency of individual sorting and coruscation factor. Therefore, this research is aimed to improve the performance of the previous method in identifying the ripeness of palm tree based on thermal imaging. The excess of thermal imaging was not related to the coruscation since the level of ripeness was both determined by the temperature and colour. The detection method of this research deployed the colour-based features that are Dominant Colour Descriptor and Color Moment. The DCD  and Color Moment was the input to the K-Nearest Neighbor (KNN) method.  The percentage of identification rate was 89%, and the identification of oil palm maturity level using thermal imaging is more efficient because it is done without human intervention and does not depend on lighting assistance compared to manual method and method of using RGB visual images.


Palm oil; Ripeness; DWD; Color Moment; KNN

Full Text:




M. Makky, “A Portable Low-cost Non-destructive Ripeness Inspection for Oil Palm FFB,” Ital. Oral Surg., vol. 9, pp. 230–240, 2016.

R, Rohendar, “ Pendugaan Tingkat Kematangan Buah Kelapa Sawit (Elaeis guineenis jacq) Berdasarkan Pengolahan Citra Menggunakan Metode Logika Fuzzy,” Fakultas Pertanian, Universitas Syiah Kuala, 2013.

M. Makky and P. Soni, “In situ quality assessment of intact oil palm fresh fruit bunches using rapid portable non-contact and non-destructive approach,” J. Food Eng., vol. 120, pp. 248–259, 2014.

N. Fadilah, et al., “Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch,” Sensors, doi. 10.3390/s121014179, Malaysia, December 2012

Z. May and M. H. Amaran, “Automated Ripeness Assessment of Oil Palm Fruit Using RGB and Fuzzy Logic Technique,” pp. 52–59.

M. K. Shabdin, et al., “A study on the oil palm fresh fruit bunch ( FFB ) ripeness detection by using Hue , Saturation and Intensity ( HSI ) approach A study on the oil palm fresh fruit bunch ( FFB ) ripeness detection by using Hue , Saturation and Intensity ( HSI ) approach,” Earth Environ, sci. 37012039, 2016.

M. Shariff, et al., “Comparison of mean temperature taken between commercial and prototype thermal sensor in estimating mean temperature of oil palm fresh fruit bunches,” IFRJ., vol. 23, no. December, pp. 91–95, 2016.

J. Fraden, “Handbook of Modern Sensors Physics, Designs, and Applications,” Fourth Edi., New York Heidelberg Dordrecht London: April, 2010.

L. Zhang and A. Li, "Region-of-Interest Extraction Based on Saliency Analysis of Co-Occurrence Histogram in High Spatial Resolution Remote Sensing Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 5, pp. 2111-2124, May 2015.

Y. Dhyanti, K. Munadi, and F. Arnia, “Penerapan Deskriptor Warna Dominan untuk Temu Kembali Citra Busana pada Peranti Bergerak,” Jurnal Rekayasa Elektrika, Vol. 12, No. 3, pp, 104-110, Des. 2016.

M. Oktiana, F. Arnia and K. Munadi, "Retrieval performance of color descriptors derived from DC components of protected JPEG images," 2016 Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), Bali, 2016, pp. 53-59.

V. Vinayak, “CBIR System using Color Moment and Color Auto-Correlogram with Block with Block Truncation Coding,” International Journal of Computer Application, vol.161, no.9, pp. 0975-8887, March 2017.

Z. Zhang, T. Jiang, S. Li, and Y. Yang, “Automated feature learning for nonlinear process monitoring – An approach using stacked denoising autoencoder and k-nearest neighbor rule,” J. Process Control, vol. 64, pp. 49–61, 2018.



  • There are currently no refbacks.

View My Stats