Artificial Intelligence, Automation, and Digitalization in Fresh Fruit Bunch Grading for the Palm Oil Industry: A Systematic Literature Review
Main Article Content
Accounting for 85% of global palm oil production, Indonesia holds a dominant position as the industry’s leading 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 introduces evaluation bias, output variability, and measurable financial inefficiencies at the mill level. Following the PRISMA 2020 protocol, this study conducted a Systematic Literature Review (SLR) to consolidate and critically evaluate empirical evidence on the application of artificial intelligence (AI), automation, and digitalization in FFB grading operations. Three peer-reviewed databases—Scopus, ScienceDirect, and Google Scholar—were systematically searched to retrieve publications from 2018 to 2025. After multistage eligibility screening and quality appraisal using the Mixed Methods Appraisal Tool (MMAT) 2018, a total of 29 methodologically sound articles were included in the synthesis, supported by strong inter-rater reliability (Cohen’s Kappa, ? = 0.82), indicating substantial agreement. The synthesis identified four key findings: (1) CNN- and YOLO-based architectures achieved FFB classification accuracy of up to 97%, demonstrating substantial performance advantages over manual grading; (2) three dimensions—technological readiness, organizational readiness, and human capability—consistently determined implementation outcomes, with organizational readiness alone increasing adoption rates by 15–20%; (3) AI-driven grading systems generated measurable improvements in Oil Extraction Rate (OER) and reductions in Free Fatty Acid (FFA) levels, with economic benefits typically realized within 12–24 months of deployment; (4) despite its strength in confirmatory analysis, CB-SEM remains underutilized in this research domain. One of the most important findings of this review is the absence of a unified structural model linking mill readiness, technology adoption pathways, and downstream business performance outcomes—a gap that is both theoretically significant and practically urgent. To address this issue, this review proposes a structured empirical research agenda to guide future studies in the Indonesian palm oil industry context.
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
