Pencitraan Hiperspekral untuk Membedakan Asal Tanah Tumbuh Dari Tandan Buah Segar Kelapa Sawit

 Dina Veranita (Universitas Riau, Pekanbaru, Indonesia)
 (*)Minarni Shiddiq Mail (Universitas Riau, Pekanbaru, Indonesia)
 Feri Candra (Universitas Riau, Pekanbaru, Indonesia)
 Saktioto Saktioto (Universitas Riau, Pekanbaru, Indonesia)
 Mohammad Fisal Rabin (Universitas Riau, Pekanbaru, Indonesia)

(*) Corresponding Author



Hyperspectral imaging is a non destructive method that has been used to evaluate internal characteristics of fruits and vegetables. Plant genetics, soil characteristics, and plant management are some of key factors to define the quality of oil palm fresh fruit bunches (FFB) produced. This research was aimed to discriminate the Tenera oil palm FFBs produced by oil palm trees grown from mineral soil and peat soil using a hyperspectral imaging system which utilized a Specim V10 spektrograf. The discrimination was based on their ripeness level, mesocarp firmness, and classification using K-mean clustering. The samples consisted of 61 mineral soil FFBs and 60 peat soil FFBs with three ripeness levels as unripe, ripe, and overripe. Hyperspectral images were recorded and processed using Matlab programs. The spectral reflectance intensities showed the discrimination between both origin soils at wavelength ranges of 700 nm  900 nm. The results also showed higher reflectance intensities of peat soil FFBs than mineral soil FFBs. Correspondingly, Fruit firmness of peat soil FFBs are higher than mineral soil FFBs. Classification using K- mean clustering between reflectance intensities and fruit firmness showed significant clusters for three ripeness levels. These results will be useful for an oil palm FFB sorting machine based on spectral imaging method


Hyperspectral Imaging, Oil Palm, Fresh Fruit Bunches, Firmness Level, K-Mean Clustering

Full Text:


Article Metrics

Abstract view : 128 times
PDF - 44 times


J. AKodagali and S. Balaji, “Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits A Review,” Int. J. Comput. Appl., 2012

A. Bhargava and A. Bansal, “Fruits and vegetables quality evaluation using computer vision: A review,” J. King Saud Univ. - Comput. Inf. Sci., 2018, doi: 10.1016/j.jksuci.2018.06.002.

B. Zhang, W. Huang, J. Li, C. Zhao, S. Fan, J. Wu, C, Liu, “Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review,” Food Research International. 2014, doi: 10.1016/j.foodres.2014.03.012.

M. P. Arakeri and Lakshmana, “Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry,” in Procedia Computer Science, 2016

Abdellahhalimi, A. Roukhe, B. Abdenabi, and N. El Barbri, “Sorting dates fruit bunches based on their maturity using camera sensor system,” J. Theor. Appl. Inf. Technol., 2013.

J. A. Abbott, “Quality measurement of fruits and vegetables,” Postharvest Biol. Technol., 1999, doi: 10.1016/S0925-5214(98)00086-6.

P. Pathmanaban, B. K. Gnanavel, and S. S. Anandan, “Recent application of imaging techniques for fruit quality assessment,” Trends in Food Science and Technology, Vol 94 pp 32-42, 2019,.

A. Hussain, H. Pu, and D. W. Sun, “Innovative nondestructive imaging techniques for ripening and maturity of fruits – A review of recent applications,” Trends in Food Science and Technology, Vol 72, pp 144-152, 2018.

Y. Lu, Y. Huang, and R. Lu, “Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: A review,” Applied Sciences (Switzerland). 2017, doi: 10.3390/app7020189.

R. Gruna, K. Vieth, M. Michelsburg, and F. Puente León, “Hyperspectral Imaging – From Laboratory to In-line Food Sorting,” in 2nd International Workshop on Image Analysis in Agriculture 2010, 2010.

J. Xue, S. Zhang, and J. Zhang, “Ripeness classification of Shajin apricot using hyperspectral imaging technique,” Nongye Gongcheng Xuebao/Transactions Chinese Soc. Agric. Eng., 2015, doi: 10.11975/j.issn.1002-6819.2015.11.043.

G. El Masry, N. Wang, A. ElSayed dan M. Ngadi, “ Hyperspectral imaging for non destructive determination of some quality attributes for strawberry,” J. Food Eng, vol 81 pp. 98-107, 2007.

A. Rahman et al., “Nondestructive estimation of moisture content, pH and soluble solid contents in intact tomatoes using hyperspectral imaging,” Appl. Sci., vol. 7 p109, 2017, doi: 10.3390/app7010109.

[14]. Q. Lü and M. Tang, “Detection of Hidden Bruise on Kiwi fruit Using Hyperspectral Imaging and Parallelepiped Classification,” Procedia Environ. Sci., 2012, doi: 10.1016/j.proenv.2012.01.404.

M. Hudori, “Dampak Kerugian dan Usulan Pemecahan Masalah Kualitas Crude Palm Oil (CPO) di Pabrik Kelapa Sawit,” Industrial Eng J. vol. 5(1) pp.35-40, 2016

H. Ishak, M. Shiddiq, R. H. Fitra, and N. Z. Yasmin, “Ripeness Level Classification of Oil Palm Fresh Fruit Bunch Using Laser Induced Fluorescence Imaging,” J. Aceh Phys. Soc., 2019, doi: 10.24815/jacps.v8i3.14139.

M. H. Razali, A. S. M. . Halim, and S. Roslan, “A review on crop plant production and ripeness forecasting,” Int. J Agric. Crop Sci., vol.4(2) pp. 54-36, 2012.

D. Tyas and B. Hermawan, “Hubungan antara Beberapa Karakteristik Fisik Lahan dan Produksi Kelapa Sawit Relations between Physical Characteristics of Land and Palm Oil Production,” Akta Agrosia, 2010.

E. Nugraheni and N. Pangaribuan, “Pengelolaan lahan pertanian gambut secara berkelanjutan,” Univ. Terbuka, Tangerang Selatan Univ. Pajajaran, 2008.

A. Dariah, E. Maftuah, and Maswar, “Karakteristik Lahan Gambut,” Pandu. Pengelolaan Berkelanjutan Lahan Gambut Terdegradasi, 2013.

N. Fadilah, J. Mohamad-Saleh, Z. A. Halim, H. Ibrahim, and S. S. S. Ali, “Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch,” Sensors (Switzerland), 2012, doi: 10.3390/s121014179.

M. H. M. Hazir, A. R. M. Shariff, M. D. Amiruddin, A. R. Ramli, and M. Iqbal Saripan, “Oil palm bunch ripeness classification using fluorescence technique,” J. Food Eng., 2012, doi: 10.1016/j.jfoodeng.2012.07.008.

F. M. A. Mazen and A. A. Nashat, “Ripeness Classification of Bananas Using an Artificial Neural Network,” Arab. J. Sci. Eng., 2019, doi: 10.1007/s13369-018-03695-5.

P. Junkwon, T. Takigawa, H. Okomoto et al., “Hyperspectral imaging for nondestructive determination of internal qualities for oil palm (Elaeis guineensis Jacq. var. tenera),” Agric. Inf. Res., vol. 18 (3) pp. 130-141.

O. M. Bensaeed, A. M. Shariff, A. B. Mahmud, H. Shafri, and M. Alfatni, “Oil palm fruit grading using a hyperspectral device and machine learning algorithm,” in IOP Conference Series: Earth and Environmental Science, pp. 1-22, 2014.

M. H. M. Hazir and A. R. M. Shariff, “Oil palm physical and optical characteristics from two different: Planting materials,” Res. J. Appl. Sci. Eng. Technol., vo. 3 (9) pp. 953-952, 2011.

J. Yadav and M. Sharma, “A Review of K-mean Algorithm,” Int. J. Eng. Trends Technol., 2013.

H. Huang, L. Liu, and M. O. Ngadi, “Recent developments in hyperspectral imaging for assessment of food quality and safety,” Sensors (Switzerland). 2014, doi: 10.3390/s140407248.

F. Candra and S. A. R. S. Abu Bakar, “Hyperspectral imaging for predicting soluble solid content of starfruit,” J. Teknol., vol.73(1) pp. 83-87, 2015, doi: 10.11113/jt.v73.3480.

G. ElMasry and D. W. Sun, “Principles of Hyperspectral Imaging Technology,” in Hyperspectral Imaging for Food Quality Analysis and Control, 2010.

S.S. Sivakumar, J. Qiao, N. Wang, Y. Gariépy, G.S.V. Raghavan, and James McGill, “Detecting Maturity Parameters of Mango Using Hyperspectral Imaging Technique,” 2013, doi: 10.13031/2013.21532.

S. Fan, W. Huang, Z. Guo, B. Zhang, and C. Zhao, “Prediction of Soluble Solids Content and Firmness of Pears Using Hyperspectral Reflectance Imaging,” Food Anal. Methods, 2015, doi: 10.1007/s12161-014-0079-1.

L. Morissette and S. Chartier, “The k-means clustering technique: General considerations and implementation in Mathematica,” Tutor. Quant. Methods Psychol., 2013, doi: 10.20982/tqmp.09.1.p015)

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Pencitraan Hiperspekral untuk Membedakan Asal Tanah Tumbuh Dari Tandan Buah Segar Kelapa Sawit


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email :

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.