Article Sidebar

Submitted
Jul 1, 2025
Accepted
Jul 24, 2025
Published
Jul 31, 2025
Download
pdf
Statistic
Read Counter : 20 Download : 6

Downloads

Download data is not yet available.

Main Article Content

Abstract

Determining the quality of oil palm Fresh Fruit Bunches (FFB) quickly and accurately is very important in the grading process to ensure the quality of production results and the efficiency of the post-harvest process. This study aims to evaluate the quality of oil palm FFB non-destructive using thermal image technology, focusing on two main parameters: moisture content and oil content. The oil palm FFB used was the Tenera variety. Thermal characteristic data were obtained from the RGB pseudo color thermal images and the oil palm FFB temperature. The model obtained using Artificial Neural Network (ANN) showed that the calibration model for moisture content produced a linear regression equation y = 0.9826x + 0.7159 (R² = 0.9827), and for oil content y = 0.9962x + 0.0289 (R² = 0.9973). At the validation stage, the moisture content prediction model gave y = 0.9056x + 10.721 (R² = 0.8908), and oil content y = 0.7683x + 1.6494 (R² = 0.8567). These results indicated that thermal imaging technology has great potential as an efficient and accurate non-destructive method in evaluating the quality of oil palm FFB, especially in supporting a more objective and sustainable grading system.  

Keywords

FFB Non-destructive ANN grading

Article Details

Creative Commons License

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

How to Cite
1.
Sirtin AA, Makky M, Santosa S, Cherie D. Non-Destructive Evaluation Quality of Oil Palm Fresh Fruit Bunch (FFB) (Elaeis guineensis Jacq.) Using Thermal Imaging in the Grading Process . EKSAKTA [Internet]. 2025 Jul. 31 [cited 2025 Aug. 14];26(03):312-28. Available from: https://eksakta.ppj.unp.ac.id/index.php/eksakta/article/view/611

References

  1. [1] BPS - Statistics Indonesia. (2024). Luas areal perkebunan kelapa sawit di Indonesia 2023. Jakarta, Indonesia: BPS.
  2. [2] Indra, I., Sukmana, O., Wahyudi, W., & Fachrina, Y. (2024). Fluctuation price sell palm farmers and impact socioeconomic society farmer palm oil in Rokan Hilir Regency Province Riau. Asian Journal of Engineering, Social and Health.
  3. [3] Hidayat, A., Robiani, B., Marwa, T., Suhel, S., Susetyo, D., & Mukhlis, M. (2024). A Crude Palm Oil Industry Concentration and Influencing Factors: A Case Study of Indonesia as the World's Largest Producer. Agris on-line Papers in Economics and Informatics.
  4. [4] Ghofar, A., Rosjidi, M., Setiadi, S., Iswantini, D., & Mulijani, S. (2024). Study on Palm Oil Cultivation and Processing Technology to Support The Biofuel Program (Optimization for The Development of Palm Oil-Based Energy Plantation in Indonesia). IOP Conference Series: Earth and Environmental Science, 1364.
  5. [5] Limaho, H. (2023). The Strategy of Palm Oil Plantation Expansion Business in Relation to Environmental Sustainability Issues: Overcoming The Challenges. Eduvest - Journal of Universal Studies.
  6. [6] United States Department of Agriculture (USDA). (2024). World agricultural production: Palm oil update. Washington, D.C., USA: USDA.
  7. [7] Fosch, A., De Arruda, G., Aleta, A., Descals, A., Gaveau, D., Morgans, C., ... & Moreno, Y. (2023). Replanting unproductive palm oil with smallholder plantations can help achieve Sustainable Development Goals in Sumatra, Indonesia. Communications Earth & Environment.
  8. [8] Sari, D., Ramadhan, T., & Putra, R. (2019). Analysis of oil content in fresh fruit bunches (FFB) using non-destructive methods. Jurnal Teknologi Pertanian, 12(2), 45–52.
  9. [9] Azima, A., Nugroho, Y. S., & Hidayat, A. (2019). Detection of oil palm fruit ripeness using thermal image processing. International Journal of Advanced Computer Science and Applications, 10(4), 187–192.
  10. [10] Zolfagharnassab, S., Shariff, A., Ehsani, R., Jaafar, H., & Aris, I. (2022). Classification of oil palm fresh fruit bunches based on their maturity using thermal imaging technique. Agriculture.
  11. [11] Fauziah, W. K. (2021). Evaluasi non-destruktif kualitas tandan buah segar (TBS) kelapa sawit (Elaeis guineensis Jacq) berdasarkan sifat termal (Tesis Magister, Universitas Andalas).
  12. [12] Junos, M., Khairuddin, A., Talip, M., Kairi, M., & Siran, Y. (2024). Improved hybrid feature extractor in lightweight convolutional neural network for postharvesting technology: Automated oil palm fruit grading. Neural Computing and Applications, 36, 20473–20491.
  13. [13] Badan Standardisasi Nasional. (1992). SNI 01-2891-1992: Metode pengujian minyak dan lemak. Jakarta: BSN.
  14. [14] Hasibuan, H. A., & Rivani, M. (2015). Penentuan rendemen crude palm oil (CPO) dan kernel dari buah sawit di kebun dan pabrik kelapa sawit. Warta PPKS, 20(3), 99–104.
  15. [15] American Oil Chemists’ Society (AOCS). (1989). Official methods and recommended practices of the AOCS (4th ed.). Urbana, IL: AOCS Press.
  16. [16] Melidawati, R. (2021). Penerapan artificial neural network untuk prediksi kualitas TBS kelapa sawit berdasarkan citra termal. Jurnal Agroindustri, 10(2), 123–130.
  17. [17] Guspa, M., Makky, M., & Cherie, D. (2024). Non-destructive evaluation of oil and free fatty acid content of oil palm fresh fruit bunch based on thermal properties using partial least square (PLS). Jurnal Teknik Pertanian Lampung.
  18. [18] Chin-Hashim, N., Khaled, A., Jamaludin, D., & Aziz, A. (2022). Electrical Impedance Spectroscopy for Moisture and Oil Content Prediction in Oil Palm (Elaeis guineensis Jacq.) Fruitlets. Plants, 11.
  19. [19] Rahmi, S., Cherie, D., Ifmalinda, I., & Makky, M. (2024). Non-destructive evaluation of oil content and carotene in oil palm fresh fruit bunches based on optical properties using partial least square (PLS). Jurnal Teknik Pertanian Lampung.
  20. [20] Costa, A., Gonçalves, J., Neta, H., Alves, D., Franceschi, E., Dariva, C., Borges, G., & Leite, M. (2021). Machine learning applied as an in-situ monitoring technique for the water content in oil recovered by means of NIR spectroscopy. , 2, 405-416. https://doi.org/10.46932/SFJDV2N1-030.
  21. [21] Adiarifia, N., Budiastra, I. W., & Mardjan, S. S. (2024). Non-destructive prediction of chemical content in palm oil fruit using near-infrared spectroscopy and artificial neural network. Jurnal Keteknikan Pertanian, 12(1), 128–139.
  22. [22] Waloyo, H. T., Mujianto, A., & Feriyanto, R. (2024). Tensile strength prediction of empty palm oil bunch fiber composite with artificial neural network. Journal of Energy, Mechanical, Material, and Manufacturing Engineering, 9(2), 77–84.
  23. [23] Chong, D. J. S., Chan, Y. J., Arumugasamy, S. K., Yazdi, S. K., & Lim, J. W. (2023). Optimisation and performance evaluation of RSM, ANN and ANFIS in the prediction of biogas production from palm oil mill effluent (POME). Energy, 266, 126449.
  24. [24] Alfah, N., Munawar, A. A., & Zulfahrizal. (2022). Teknologi NIRS untuk memprediksi jumlah pencampuran minyak sawit dalam minyak nilam menggunakan metode PCR. Jurnal Ilmiah Mahasiswa Pertanian, 7(4), 1–8.
  25. [25] Sani, F., Fahmi, F., & Siregar, Y. (2023). Shelf-Life Prediction of Oil Palm Fresh Fruit Bunches Using Artificial Neural Networks. 2023 7th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM), 162-166.
  26. [26] Adedayo, O., Onibonoje, M., & Isa, M. (2021). A layer-sensitivity based artificial neural network for characterization of oil palm fruitlets. International Journal of Applied Science and Engineering, 18, 1-7.
  27. [27] Cherie, D., Fatmawati, N., & Makky, M. (2021). Non-destructive evaluation of oil palm fresh fruit bunch quality using thermal vision. IOP Conference Series: Earth and Environmental Science, 644.
  28. [28] Novianty, I., Baskoro, R., Nurulhaq, M., & Nanda, M. (2022). Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits. Information Processing in Agriculture.
  29. [29] Budiastra, I. W., Mardjan, S. S., Adiarifia, N., Novianti, I., & Suci, Y. T. (2024). Non-destructive prediction of oil and free fatty acid of oil palm fruitlets using near-infrared spectroscopy and hybrid calibration method. INMATEH – Agricultural Engineering, 73(2), 463–472.
  30. [30] Mellyana, V., Budiastra, W., Aris, Y., & P., (2024). Electrical properties for non-destructive determination of free fatty acid and moisture content in oil palm fruit. International Journal on Advanced Science, Engineering and Information Technology.
  31. [31] Kuswidianto, R., Rudiati, E. M., & Nugroho, J. W. (2014). Penentuan kadar air dan asam lemak bebas buah sawit berdasarkan parameter warna menggunakan jaringan syaraf tiruan (Skripsi, Universitas Gadjah Mada). https://etd.repository.ugm.ac.id/penelitian/detail/76994
  32. [32] Fauziah, W., Makky, M., S., & Cherie, D. (2021). Thermal vision of oil palm fruits under difference ripeness quality. IOP Conference Series: Earth and Environmental Science, 644.
  33. [33] Makky, M., & Cherie, D. (2021). Pre-harvest oil palm FFB nondestructive evaluation technique using thermal-imaging device. IOP Conference Series: Earth and Environmental Science, 757.