Artificial Intelligence, Automation, and Digitalization in Fresh Fruit Bunch Grading for the Palm Oil Industry: A Systematic Literature Review
DOI:
https://doi.org/10.58344/locus.v5i7.5907Keywords:
Systematic Literature Review, Artificial Intelligence, Automation, Digitalization, FFB Grading, Palm Oil Industry, PRISMA 2020, Industry 4.0, CB-SEMAbstract
Background: Accounting for 85% of worldwide palm oil production, Indonesia holds an unrivaled position as the industry’s dominant global supplier. Despite this prominence, Fresh Fruit Bunch (FFB) grading - the quality critical process of assessing fruit ripeness prior to processing remains largely dependent on manual operator judgment, a practice that inherently generates evaluation bias, output variability, and measurable financial inefficiencies at the mill level. Objective: Employing the PRISMA 2020 protocol as its methodological backbone, this paper conducts a Systematic Literature Review (SLR) to consolidate and critically evaluate available empirical evidence concerning the deployment of artificial intelligence (AI), automation, and digitalization within FFB grading operations. Methods: Three peer-reviewed databases: Scopus, ScienceDirect, and Google Scholar were systematically searched to retrieve publications spanning 2018 to 2025. Upon completion of multistage eligibility screening and quality evaluation using the Mixed Methods Appraisal Tool (MMAT) 2018, a corpus of 29 methodologically sound articles was retained for synthesis, supported by strong inter-rater consistency (Cohen’s Kappa k=0.82), almost perfect agreement). Results: The synthesis converged on four principal findings: (1) CNN and YOLO-based architectures deliver FFB classification accuracy reaching 97%, representing a substantial performance advantage over manual evaluation; (2) three dimensions - Technology Readiness, Organizational Readiness, and Human Capability consistently determine implementation outcomes, with organizational readiness associated with projected adoption rate growth of 15–20% over five years; (3) AI-driven grading systems produce verifiable gains in Oil Extraction Rate (OER) alongside reductions in Free Fatty Acid (FFA) levels, with quantifiable business value materializing within an extended post-implementation period consistent with the IT productivity paradox; (4) despite its methodological superiority for confirmatory theory testing, CB-SEM remains strikingly underemployed in this research domain. Conclusion: Among the most consequential findings of this review is the absence of a unified structural model capable of linking mill readiness dimensions, technology adoption pathways, and downstream business performance outcomes, a gap whose resolution is both theoretically necessary and practically urgent. To address this, the review presents a systematic empirical agenda oriented toward future investigation within the Indonesian palm oil industry context.
References
Abbasi, R., Martinez, P., & Ahmad, R. (2022). The digitization of agricultural industry: A systematic literature review on agriculture 4.0. Smart Agricultural Technology, 2, 100042. https://doi.org/10.1016/j.atech.2021.100042
Al Riza, D. F., Yusuf, A., & Kusuma, W. A. (2025). Comparative study of citrus fruits detection and counting with single and double labels based on convolutional neural network using YOLOv7. Smart Agricultural Technology, 10, 100589. https://doi.org/10.1016/j.atech.2024.100589
Alfatni, M. S. M., Khairunniza-Bejo, S., Marhaban, M. H. B., Ben Saaed, O. M., Mustapha, A., & Shariff, A. R. M. (2022). Towards a real-time oil palm fruit maturity system using supervised classifiers based on feature analysis. Agriculture, 12(9), 1461. https://doi.org/10.3390/agriculture12091461
Anizar, A., Matondang, A. R., Ismail, R., & Matondang, N. (2022). The role of technical support and effective communication to successful intervention program on palm oil mills. International Journal on Advanced Science, Engineering and Information Technology, 12(3), 987-993. https://doi.org/10.18517/ijaseit.12.3.15268
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
Bhat, I. A., Mir, S. A., & Shah, M. A. (2025). Leveraging artificial intelligence in agribusiness: Strategic management practices and future prospects. Agricultural Systems, 224, 104213.
Chuquimarca, L., Farinango, M., Quinga, B., & Lara-Cueva, R. (2024). Deep learning for fresh fruit bunch ripeness classification with data augmentation. Computers and Electronics in Agriculture, 218, 108670.
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Finger, R., Slijper, T., Ronner, E., Poortvliet, P. M., & de Mey, Y. (2023). Digital innovations for sustainable and resilient agricultural systems. European Review of Agricultural Economics, 50(4), 1238-1270. https://doi.org/10.1093/erae/jbad011
Gabriel, A., & Gandorfer, M. (2023). Adoption of digital technologies in agriculture: An inventory in a European small-scale farming region. Precision Agriculture, 24(1), 68-91. https://doi.org/10.1007/s11119-022-09931-1
GAPKI. (2023). Laporan kinerja industri kelapa sawit nasional 2023 [National palm oil industry performance report 2023]. Gabungan Pengusaha Kelapa Sawit Indonesia.
Gudergan, S. P., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2025). Advanced issues in covariance-based structural equation modeling. Journal of Business Research, 178, 115029. https://doi.org/10.1016/j.jbusres.2024.115029
Hair, J. F., Sarstedt, M., & Ringle, C. M. (2025). Covariance-based structural equation modeling (CB-SEM): A SmartPLS 4 software tutorial. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-025-00414-6
Hariyanti, F., Syahza, A., Zulkarnain, & Nofrizal. (2024). Economic transformation based on leading commodities through sustainable development of the oil palm industry. Heliyon, 10(4), e25674. https://doi.org/10.1016/j.heliyon.2024.e25674
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hong Tzuan, L., Mohd Hashim, U., & Abdul Aziz, S. (2022). Rapid and non-destructive determination of oil palm ripeness using NIR spectroscopy and artificial neural network. Postharvest Biology and Technology, 193, 112021.
Hong, Q. N., Fabregues, S., Bartlett, G., Boardman, F., Cargo, M., Dagenais, P., & Pluye, P. (2018). The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Education for Information, 34(4), 285-291. https://doi.org/10.3233/EFI-180221
Josdaan, J., Lestari, A. F., & Sari, R. F. (2024). Revolutionizing palm oil ripeness classification utilizing YOLOv8 for ultra-precise ripeness detection. Computers and Electronics in Agriculture, 220, 108900.
Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering (Version 2.3). EBSE Technical Report. Keele University and University of Durham.
Lai, J. W., Ramli, H. R., Ismail, L. I., & Hasan, W. Z. W. (2023). Oil palm fresh fruit bunch ripeness detection methods: A systematic review. Agriculture, 13(1), 156. https://doi.org/10.3390/agriculture13010156
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
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. https://doi.org/10.14716/ijtech.v13i6.5932
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Patkar, G. S., Lim, S. L., Parthasarathy, S., & Ibrahim, I. (2018). Challenging issues in automated oil palm fruit grading. Journal of Food Engineering, 220, 24-31.
Ringle, C. M., Sarstedt, M., Sinkovics, N., & Sinkovics, R. R. (2023). A perspective on using partial least squares structural equation modelling in data articles. Data in Brief, 48, 109074. https://doi.org/10.1016/j.dib.2023.109074
Rosbi, S., Yusoff, N., Zakaria, M. H., & Ibrahim, I. (2024). Classification of oil palm fresh fruit bunches using machine learning ensemble approach. Computers and Electronics in Agriculture, 215, 108427.
Siallagan, S., & Ishak, A. (2022). A technological capability assessment of company in the crude palm oil industry in Indonesia. IOP Conference Series: Earth and Environmental Science, 1025, 012012. https://doi.org/10.1088/1755-1315/1025/1/012012
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339. https://doi.org/10.1016/j.jbusres.2019.07.039
Solow, R. M. (1987, July 12). We'd better watch out. New York Times Book Review, 36.
Suharjito, Junior, F. A., Koeswandy, Y. P., Debi, Nurhayati, P. W., Asrol, M., & Marimin. (2023). Annotated datasets of oil palm fruit bunch piles for ripeness grading using deep learning. Scientific Data, 10(1), 72. https://doi.org/10.1038/s41597-023-01958-x
Tan, C. X., Lim, W. S., & Choo, Y. M. (2023). Free fatty acid formation in oil palm fresh fruit bunches during the post-harvest period. Agriculture, 13(4), 812. https://doi.org/10.3390/agriculture13040812
Uren, V., & Edwards, J. S. (2023). Technology readiness and the organizational journey towards AI adoption: An empirical study. International Journal of Information Management, 68, 102588. https://doi.org/10.1016/j.ijinfomgt.2022.102588
Xu, J. (2024). Sustainable agriculture in the digital era: Past, present, and future trends by bibliometric analysis. Computers and Electronics in Agriculture, 217, 108628.
Zaki, M. A. M., Anuar, M. S., Rashid, M. H. A., & Muharam, F. M. (2024). Impact of Industry 4.0 technologies on the oil palm industry: A literature review. Computers and Electronics in Agriculture, 218, 108700.
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3(5), 616-630. https://doi.org/10.1016/J.ENG.2017.05.015
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 technique. Agriculture, 12(11), 1779. https://doi.org/10.3390/agriculture12111779
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ferizal, Bambang Iskandriawan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA). that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
