Maturity Classification Methods for Palm Oil Fresh Fruit Bunch: A Systematic Review
DOI:
https://doi.org/10.70609/gtech.v8i4.5050Keywords:
maturity classification, fresh fruit bunches, palm oil, deep learningAbstract
Determining the maturity of palm oil fruit is very important to improve the quality and quantity of palm oil production. This research examines the use of deep learning technology to classify oil palm maturity through a Systematic Literature Review (SLR). The research method used is a Systematic Literature Review (SLR) which involves analysis of 35 journals from Scopus and Google Scholar from 2020 to 2024, with a focus on datasets, algorithms, dataset locations, and methods for measuring model performance. The results show that ANN and CNN are the most widely used algorithms, with usage of 16% and 10% respectively. Accuracy, precision, recall, and F1 score are the most common performance metrics. Future research should focus on improving model generalization and integrating data from multiple sources to improve classification accuracy, the aim of which is to contribute to palm oil maturity classification and help the industry improve production efficiency and quality
References
Alfatni, M. S. M., Mohamed Shariff, A. R., Ben Saaed, O. M., & Albhbah, A. M., & Mustapha, A. (2020). Colour feature extraction methods for real-time system of oil palm fresh fruit bunch maturity grading. IOP Conference Series: Earth and Environmental Science, 540, 012092.
Aliteh, N. A., Minakata, K., Tashiro, K., Wakiwaka, H., Kobayashi, K., Nagata, H., & Misron, N. (2020). Fruit battery method for oil palm fruit ripeness sensor and comparison with computer vision method. Sensors, 20, 637.
Azmi, M. H. I. M., Hashim, F. H., Huddin, A. B., & Sajab, M. S. (2022). Correlation study between the organic compounds and ripening stages of oil palm fruitlets based on the Raman spectra. Sensors, 22, 7091.
Bonet, I., Gongora, M., Acevedo, F., & Ochoa, I. (2024). Deep learning model to predict the ripeness of oil palm fruit. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) (Vol. 3, pp. 1068-1075). SCITEPRESS – Science and Technology Publications, Lda. https://doi.org/10.5220/0012434600003636
Databoks. (2024, Juni 6). Luas perkebunan sawit Indonesia tumbuh 56% dalam sedekade. Katadata. Diakses pada 16 Juni 2024, dari https://databoks.katadata.co.id/datapublish/2024/06/06/luas-perkebunan-sawit-indonesia-tumbuh-56-dalam-sedekade
Fauziah, W. K., Makky, M., Santosa, & Cherie, D. (2021). Thermal vision of oil palm fruits under different ripeness quality. IOP Conference Series: Earth and Environmental Science, 644, 012044.
Herman, H., Susanto, A., Cenggoro, T. W., Suharjito, & Pardamean, B. (2020). Oil palm fruit image ripeness classification with computer vision using deep learning and visual attention. Journal of Telecommunications and the Digital Economy, 12, 21–27.
Husin, H. S., Amar, N., Bakar Sajak, A. A., & Sallehin Mohd Kassim, M. (2021). Distribution map of oil palm fresh fruit bunch using LiDAR. In Proceedings of the 2021 12th International Conference on Information and Communication Systems (ICICS) (pp. 4–9). Valencia, Spain.
Junos, M. H., Mohd Khairuddin, A. S., Thannirmalai, S., & Dahari, M. (2021). An optimized YOLO-based object detection model for crop harvesting system. IET Image Processing, 15, 2112–2125.
Makky, M., & Cherie, D. (2021). Pre-harvest oil palm FFB nondestructive evaluation method using thermal-imaging device. IOP Conference Series: Earth and Environmental Science, 757, 012003.
Mohd Ali, M., Hashim, N., & Abdul Hamid, A. S. (2020). Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity. Computers and Electronics in Agriculture, 169, 105235.
Pusadan, M. Y., Safitri, I., & Wirdayanti. (2023). The image extraction using the HSV method to determine the maturity level of palm oil fruit with the k-nearest neighbor algorithm. JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(6), 1448-1456. http://jurnal.iaii.or.id
Raj, T., Hashim, F. H., Huddin, A. B., Hussain, A., Ibrahim, M. F., & Abdul, P. M. (2021). Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra. Scientific Reports, 11, 18315.
Saifullah, S., Prasetyo, D. B., Indahyani, I., Dreżewski, R., & Dwiyanto, F. A. (2023). Palm oil maturity classification using K-nearest neighbors based on RGB and Lab color extraction. In Procedia Computer Science, 225, 3011-3020. https://doi.org/10.1016/j.procs.2023.09.008
Septiarini, A., Hatta, H. R., Hamdani, H., Oktavia, A., Kasim, A. A., & Suyanto, S. (2020). Maturity grading of oil palm fresh fruit bunches based on a machine learning approach. In Proceedings of the 2020 5th International Conference on Informatics and Computing (ICIC) (pp. 19–22). Gorontalo, Indonesia.
Septiarini, A., Sunyoto, A., Hamdani, H., Kasim, A. A., Utaminingrum, F., & Hatta, H. R. (2021). Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Scientific Horticulture, 286, 110245.
Setiawan, A. W., & Prasetya, O. E. (2020). Palm oil fresh fruit bunch grading system using multispectral image analysis in HSV. In Proceedings of the 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (pp. 85–88). Doha, Qatar.
Suharjito, Asrol, M., Utama, D. N., Junior, F. A., & Marimin. (2023). Real-time oil palm fruit grading system using smartphone and modified YOLOv4. IEEE Access, 10, 3285537. https://doi.org/10.1109/ACCESS.2023.3285537
Suharjito, Elwirehardja, G. N., & Prayoga, J. S. (2021). Oil palm fresh fruit bunch ripeness classification on mobile devices using deep learning approaches. Computers and Electronics in Agriculture, 188, 106359.
Tzuan, G. T. H., Hashim, F. H., Raj, T., Huddin, A. B., & Sajab, M. S. (2022). Oil palm fruits ripeness classification based on the characteristics of protein, lipid, carotene, and guanine/cytosine from the Raman spectra. Plants, 11, 1936.
Zolfagharnassab, S., Shariff, A. R. B. M., Ehsani, R., Jaafar, H. Z., & Aris, I. B. (2022). Classification of oil palm fresh fruit bunches based on their maturity using thermal imaging techniques. Agriculture, 12, 1779.
Zamri, N. M., & Anuar, A. K. (2023). Palm fresh fruit bunches (FFBs) colour grading system using Raspberry Pi. Evolution in Electrical and Electronic Engineering, 4(2), 574-581. https://doi.org/10.30880/eeee.2023.04.02.070
Wang, H. H., Wang, Y. C., Wee, B. L., & Sim, S. W. (2024). Novel feature extraction for oil palm bunches classification. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34(1), 350-360. https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/index
Shiddiq, M., Candra, F., Anand, B., & Rabin, M. F. (2024). Neural network with k-fold cross validation for oil palm fruit ripeness prediction. TELKOMNIKA Telecommunication Computing Electronics and Control, 22(1), 164-174. https://doi.org/10.12928/TELKOMNIKA.v22i1.24845
Salim, E., & Suharjito. (2023). Hyperparameter optimization of YOLOv4 tiny for palm oil fresh fruit bunches maturity detection using genetics algorithms. Smart Agricultural Technology, 6, 100364. https://doi.org/10.1016/j.atech.2023.100364
Azman, H., & Suriani, N. S. (2023). Grading oil palm fruit bunch using convolution neural network. Evolution in Electrical and Electronic Engineering, 4(1), 185-194. https://doi.org/10.30880/eeee.2023.04.01.022
Siricharoen, P., Yomsatieankul, W., & Bunsri, T. (2023). Fruit maturity grading framework for small dataset using single image multi-object sampling and Mask R-CNN. Smart Agricultural Technology, 3, 100130. https://doi.org/10.1016/j.atech.2023.100130
Gulzar, Y. (2023). Fruit image classification model based on MobileNetV2 with deep transfer learning technique. Journal of Agricultural Technology, 12(1), 99-110. https://doi.org/10.1016/j.jatech.2023.100112
Mamat, N., Othman, M. F., Abdulghafor, R., & Alwan, A. A., & Gulzar, Y. (2023). Enhancing image annotation technique of fruit classification using a deep learning approach. Journal of Agricultural Technology, 12(1), 120-132. https://doi.org/10.1016/j.jatech.2023.100120
Suharjito, Junior, F. A., Koeswandy, Y. P., Nurhayati, P. W., Asrol, M., & Marimin. (2023). Annotated datasets of oil palm fruit bunch piles for ripeness grading using deep learning. Journal of Agricultural Technology, 12(1), 54-67. https://doi.org/10.1016/j.jatech.2023.100054
Alfatni, M. S. M., Khairunniza-Bejo, S., Marhaban, M. H. B., Ben Saaed, O. M., Mustapha, A., & Mohamed Shariff, A. R. (2022). Towards a real-time oil palm fruit maturity system using supervised classifiers based on feature analysis. Journal of Agricultural Technology, 11(1), 33-45. https://doi.org/10.1016/j.jatech.2022.100033
Ismail, N., & Malik, O. A. (2022). Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Information Processing in Agriculture, 9, 24-37. https://doi.org/10.1016/j.inpa.2022.100024
Tzuan, G. T. H., Hashim, F. H., Raj, T., Huddin, A. B., & Sajab, M. S. (2022). Oil palm fruits ripeness classification based on the characteristics of protein, lipid, carotene, and guanine/cytosine from the Raman spectra. Information Processing in Agriculture, 9(3), 24-37. https://doi.org/10.1016/j.inpa.2022.100024
Mansour, M. Y. M. A., Dambul, K. D., & Choo, K. Y. (2022). Object detection algorithms for ripeness classification of oil palm fresh fruit bunch. International Journal of Technology, 13(6), 1326-1335. http://ijtech.eng.ui.ac.id
Luddin, M. H. M., & Rahman, M. A. (2022). Comparison CNN and MobileNet_v2 model for oil palm FBB ripeness classification. Evolution in Electrical and Electronic Engineering, 3(2), 146-154. https://doi.org/10.30880/eeee.2022.03.02.018
Melidawati, et al. (2021). Non-destructive evaluation quality of oil palm fresh fruit bunch (FFB) (Elaeis guineensis Jack) based on optical properties using artificial neural network (ANN). IOP Conference Series: Earth and Environmental Science, 644, 012032. https://doi.org/10.1088/1755-1315/644/1/012032
Teh, W. Y., & Tan, I. K. T. (2021). Coloured edge maps for oil palm ripeness classification. Journal of Agricultural Technology, 11(2), 78-89.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Nurita Evitarina, Kusrini Kusrini
This work is licensed under a Creative Commons Attribution 4.0 International License.
