Downloads

https://doi.org/10.62836/jitp.2025.431
Judijanto, L. (2025). Exploring the Potentials of Artificial Intelligence and Digital Technologies in Transforming the Palm Oil Industry: A Review. Journal of Information, Technology and Policy, 1–13. https://doi.org/10.62836/jitp.2025.431
Volume 3, Issue 1, 2025
Copyright (c) 2025 Loso Judijanto
Creative Commons License

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

Exploring the Potentials of Artificial Intelligence and Digital Technologies in Transforming the Palm Oil Industry: A Review

The palm oil industry faces increasing pressure to improve productivity, sustainability, and supply chain transparency amid environmental and economic challenges. This study aims to explore the potential of artificial intelligence (AI) and digital technologies in transforming the palm oil sector by synthesising existing qualitative literature. A qualitative literature review methodology was employed, focusing on secondary data sourced from 80 peer-reviewed academic articles, institutional reports, and relevant industry publications. Data collection involved systematic document retrieval and screening to ensure relevance and credibility. Thematic analysis was conducted to identify key areas where AI and digital tools impact the industry, emphasising precision agriculture, supply chain traceability, environmental monitoring, and labour productivity. The results reveal that AI applications significantly enhance yield optimisation through advanced remote sensing and machine learning algorithms, improve supply chain transparency via blockchain and natural language processing, and support environmental compliance through satellite monitoring and emissions detection. Additionally, AI-driven automation aids labour management, addressing workforce challenges and operational efficiency. Despite these advancements, barriers such as low digital literacy among smallholders and infrastructure limitations persist, limiting widespread adoption. The study concludes that while AI and digital technologies hold transformative potential, comprehensive strategies incorporating technological innovation and capacity building are essential for inclusive sectoral development. Future research should focus on pilot implementations, socio-economic impact assessments, and the development of tailored solutions for smallholder integration to fully harness digital transformation benefits in the palm oil industry.

artificial intelligence digital technologies palm oil industry qualitative literature review sustainability

References

  1. Mithas S, Chen ZL, Saldanha TJ, et al. How Will Artificial Intelligence and Industry 4.0 Emerging Technologies Transform Operations Management? Production and Operations Management 2022; 31(12): 4475–4487. https://doi.org/10.1111/poms.13864.
  2. Gideon EM. Sustainable Agriculture Leveraging Artificial Intelligence Systems in Kenya’s Agri-Food Supply Chain: Leveraging Artificial Intelligence Systems in Kenya’s Agri-Food Supply Chain. Agricultural Science 2024; 7(2): 153–171. https://doi.org/10.55173/agriscience.v7i2.128.
  3. Maffezzoli F, Ardolino M, Bacchetti A, et al. Agriculture 4.0: A Systematic Literature Review on the Paradigm, Technologies and Benefits. Futures 2022; 142: 102998. https://doi.org/10.1016/j.futures.2022.102998.
  4. López-Quílez A. AI, IoT and Remote Sensing in Precision Agriculture. Applied Sciences 2025; 15(6): 2890. https://doi.org/10.3390/app15062890.
  5. Elufioye OA, Ike CU, Odeyemi O, et al. AI-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of AI in Forecasting Demand and Optimizing Supply in Agriculture. Computer Science & IT Research Journal 2024; 5(2): 473–497. https://doi.org/10.51594/csitrj.v5i2.817.
  6. Savaş S. Application of deep ensemble learning for palm disease detection in smart agriculture. Heliyon 2024; 10(17). https://doi.org/10.1016/j.heliyon.2024.e37141.
  7. Sediyono A, Solihah B. The Opportunity of AI Technology to Increase The Value Chain of Oil Palm Plantation. Intelmatics 2025; 5(1): 42–47. https://doi.org/10.31294/intelmatics.v5i1.22477.
  8. Wardhani R, Rahadian Y. Sustainability Strategy of Indonesian and Malaysian Palm Oil Industry: A Qualitative Analysis. Sustainability Accounting, Management and Policy Journal 2021; 12(5): 1077–1107.
  9. Alamsyah Z, Mara A, Rayesa NF, et al. Oil Palm Contribution to SDGS Achievement: A Case Study in Main oil Palm Producing Provinces in Indonesia. In Proceedings of the The 3rd International Seminar on Promoting Local Resources for Sustainable Agriculture and Development, Bengkulu, Indonesia, 24–25 September 2022; EDP Sciences: Les Ulis, France, 2023. https://doi.org/10.1051/e3sconf/202337304030.
  10. Manurung GME, Siregar YI, Syahza SA. Opportunity for Sustainable Palm Oil Practices by Smallholder Farmers in Riau. Journal of Hunan University Natural Sciences 2021; 48(10).
  11. Pribadi DO, Rustiadi E, Nurdin M, et al. Mapping Smallholder Plantation as a Key to Sustainable Oil Palm: A Deep Learning Approach to High-Resolution Satellite Imagery. Applied Geography 2023; 153: 102921. https://doi.org/10.1016/j.apgeog.2023.102921.
  12. Khan N, Kamaruddin MA, Ullah Sheikh U, et al. Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow. Plants 2022; 11(13): 1697. https://doi.org/10.3390/plants11131697.
  13. Liu W, Zhang X, Chen H, et al. Improving Supply Chain Transparency through Blockchain Technology. Annals of Operations Research 2024. https://doi.org/10.1007/s10479-024-06009-1.
  14. Mensah P, Ayidzoe M, Bawah F, et al. Promoting Digital Literacy among Farmers Unlocking the Potential of Artificial Intelligence Enabled Crop Pests Disease Detection Mobile Application. 2024.
  15. Mishra D, Muduli K, Raut R, et al. Challenges Facing Artificial Intelligence Adoption during COVID-19 Pandemic: An Investigation into the Agriculture and Agri-Food Supply Chain in India. Sustainability 2023; 15(8): 6377. https://doi.org/10.3390/su15086377.
  16. Abubakar A, Ishak MY. Exploring the Intersection of Digitalization and Sustainability in Oil Palm Production: Challenges, Opportunities, and Future Research Agenda. Environmental Science and Pollution Research 2024; 31(38): 50036–50055. https://doi.org/10.1007/s11356-024-34535-9.
  17. Eggen M, Heilmayr R, Anderson P, et al. Smallholder Participation in Zero-Deforestation Supply Chain Initiatives in the Indonesian Palm Oil Sector: Challenges, Opportunities, and Limitations. Elementa: Science of the Anthropocene 2024; 12(1): 00099. https://doi.org/10.1525/elementa.00099.
  18. Kanniah K, Yu L. Geospatial Technology for Sustainable Oil Palm Industry; CRC Press: Boca Raton, FL, USA, 2024.
  19. Haque B, Hasan R, Zihad OM. SmartOil: Blockchain and Smart Contract-Based Oil Supply Chain Management. IET Blockchain 2021; 1(2–4): 95–104. https://doi.org/10.1049/blc2.12005.
  20. Mohd Nain FN, Ahamed Hassain Malim NH, Abdullah R, et al. A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction. Algorithms 2022; 15(6): 218. https://doi.org/10.3390/a15060218.
  21. Senoo EEK, Anggraini L, Kumi JA, et al. IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities. Electronics 2024; 13(10): 1894. https://doi.org/10.3390/electronics13101894.
  22. Liakos KG, Busato P, Moshou D, et al. Machine Learning in Agriculture: A Review. Sensors 2018; 18(8): 2674. https://doi.org/10.3390/s18082674.
  23. Pandey DK, Mishra R. Towards Sustainable Agriculture: Harnessing AI for global food Security. Artificial Intelligence in Agriculture 2024; 12: 72–84. https://doi.org/10.1016/j.aiia.2024.04.003.
  24. Kumar P, Udayakumar A, Kumar AA, et al. Multiparameter Optimization System with DCNN in Precision Agriculture for Advanced Irrigation Planning and Scheduling Based on Soil Moisture Estimation. Environmental Monitoring and Assessment 2023; 195(1): 13. https://doi.org/10.1007/s10661-022-10846-2.
  25. Salman Z, Muhammad A, Piran MJ, et al. Crop-Saving with AI: Latest Trends in Deep Learning Techniques for Plant Pathology. Frontiers in Plant Science 2023; 14: 1224709. https://doi.org/10.3389/fpls.2023.1224709.
  26. Bhat SA, Huang NF. Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE Access 2021; 9: 110209–110222. https://doi.org/10.1109/ACCESS.2021.3100745.
  27. Satpathy B. Digital Transformation for Sustainable Agriculture: A Progressive Method for Smallholder Farmers. Current Science 2022; 123(12): 1436–1440. https://doi.org/10.18520/cs/v123/i12/1436-1440.
  28. Ginting B, Saidin OK, Azwar TKD. Oil Palm Industry Digitalization for Sustainable Plantation Production in Community Economic Development. IOP Conference Series: Earth and Environmental Science 2021; 782: 32066. https://doi.org/10.1088/1755-1315/782/3/032066.
  29. Irvin J, Sheng H, Ramachandran N, et al. Forestnet: Classifying Drivers of Deforestation in Indonesia Using Deep Learning on Satellite Imagery. arXiv 2020; arXiv201105479.
  30. Rodríguez AC, D’Aronco S, Schindler K, et al. Mapping Oil Palm Density at Country Scale: An Active Learning Approach. Remote Sensing of Environment 2021; 261: 112479. https://doi.org/10.1016/j.rse.2021.112479.
  31. Ikhlaq U, Kechadi T. Machine Learning-Based Nutrient Application’s Timeline Recommendation for Smart Agriculture: A Large-Scale Data Mining Approach. arXiv 2023; arXiv231012052.
  32. Falgenti K, Arkeman Y, Hambali E, et al. The Design of Blockchain Network of Palm Oil FFB Supply from Certified Farms and Traceability System of CPO from Independent Smallholders. IOP Conference Series: Earth and Environmental Science 2022; 1034: 12001. https://doi.org/10.1088/1755-1315/1034/1/012001.
  33. Gumut G, Umar BU, Abdullahi IM, et al. A Real-Time Secure-Based Oil and Gas Supply Chain Management using Blockchain. In Proceedings of the 2024 IEEE 5th International Conference on Electro-Computing Technologies for Humanity (NIGERCON), Ado Ekiti, Nigeria, 26–28 November 2024; pp. 1–6. https://doi.org/10.1109/NIGERCON56586.2024.10360349.
  34. Karumanchi Y, Prasanna GL, Mukherjee S, et al. Plantation Monitoring Using Drone Images: A Dataset and Performance Review. arXiv 2025; arXiv:250208233.
  35. Chin R, Catal C, Kassahun A. Plant Disease Detection Using Drones in Precision Agriculture. Precision Agriculture 2023; 24(5): 1663–1682. https://doi.org/10.1007/s11119-023-10014-y.
  36. Miller T, Durlik I, Kostecka E, et al. Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data. Electronics 2025; 14(4): 696. https://doi.org/10.3390/electronics14040696.
  37. Naranjo JIC, Forero-Cantor G. Socio-Technical Transition in the Palm Oil Sector: Analysis from a Multilevel Perspective in Colombia’s Municipality of Tibú. Revista de Economia e Sociologia Rural 2023; 62(2): e271345. https://doi.org/10.1590/1806-9479.2022.271345.
  38. Zekos G. Political, Economic and Legal Effects of Artificial Intelligence; Springer: Cham, Switzerland, 2022. https://doi.org/10.1007/978-3-030-94736-1.
  39. Peckham JB. The Ethical Implications of 4IR. Journal of Ethics in Entrepreneurship and Technology 2021; 1(1): 30–42.
  40. Zaki MAM, Ooi J, Ng WPQ, et al. Impact of Industry 4.0 Technologies on the Oil Palm Industry: A Literature Review. Smart Agricultural Technology 2025; 10: 100685. https://doi.org/10.1016/j.atech.2024.100685.
  41. Jamshidi EJ, Yusup Y, Hooy CW, et al. Predicting Oil Palm Yield Using a Comprehensive Agronomy Dataset and 17 Machine Learning and Deep Learning Models. Ecological Informatics 2024; 81: 102595. https://doi.org/10.1016/j.ecoinf.2024.102595.
  42. Irwansyah E, Gunawan AAS, Dzikri I. Estimating Oil Palm Tree Yield and Soil Composition Using Multi-Scale CNN and Vegetation Indices. International Journal of Intelligent Systems and Applications in Engineering 2023; 11(1): 183–189.
  43. Han M, Yi C. Deep Convolutional Neural Networks for Palm Fruit Maturity Classification. arXiv 2025; arXiv250220223.
  44. Musanase C, Vodacek A, Hanyurwimfura D, et al. Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices. Agriculture 2023; 13(11): 2141. https://doi.org/10.3390/agriculture13112141.
  45. Tanaka TS, Heuvelink GB, Mieno T, et al. Can Machine Learning Models Provide Accurate Fertilizer Recommendations? Precision Agriculture 2024; 25(4): 1839–1856. https://doi.org/10.1007/s11119-024-10136-x.
  46. Ahmadi P, Mansor SB, Ahmadzadeh Araji H, et al. Convolutional SVM Networks for Detection of Ganoderma Boninense at Early Stage in Oil Palm Using UAV and Multispectral Pleiades Images. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2023; 10: 25–30. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-25-2023.
  47. Yong LZ, Khairunniza-Bejo S, Jahari M, et al. Automatic Disease Detection of Basal Stem Rot Using Deep Learning and Hyperspectral Imaging. Agriculture 2022; 13(1): 69. https://doi.org/10.3390/agriculture13010069.
  48. Alourani A, Khan S. A Blockchain and Artificial Intelligence based System for Halal Food Traceability. arXiv 2024; arXiv241007305.
  49. Kamilaris A, Fonts A, Prenafeta-Boldύ FX. The Rise of Blockchain Technology in Agriculture and Food Supply Chains. Trends in Food Science & Technology 2019; 91: 640–652. https://doi.org/10.1016/j.tifs.2019.07.034.
  50. Ong K, Mao R, Satapathy R, et al. Explainable Natural Language Processing for Corporate Sustainability Analysis. Information Fusion 2025; 115: 102726. https://doi.org/10.1016/j.inffus.2025.102726.
  51. Wang K, Zipperle M, Becherer M, et al. An AI-Based Automated Continuous Compliance Awareness Framework (CoCAF) for Procurement Auditing. Big Data and Cognitive Computing 2020; 4(3): 23. https://doi.org/10.3390/bdcc4030023.
  52. Perdana BEG. Upgrading and Global Value Chain 4.0: The Case of Palm Oil Sector in Indonesia. Global South Review 2019; 1(2): 8–32. https://doi.org/10.22146/globalsouth.54495.
  53. Salako AO, Fabuyi JA, Aideyan NT, et al. Advancing Information Governance in AI-Driven Cloud Ecosystem: Strategies for Enhancing Data Security and Meeting Regulatory Compliance. Asian Journal of Research in Computer Science 2024; 17(12): 66–88. https://doi.org/10.9734/ajrcos/2024/v17i12530.
  54. Ghayour L, Neshat A, Paryani S, et al. Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sensing 2021; 13(7): 1349. https://doi.org/10.3390/rs13071349.
  55. Pramudya FS, Bong L, Rolling E, et al. Forest Loss Analysis and Calculation with Geospatial Artificial Intelligence: A Case Study of Papua Province. Procedia Computer Science 2023; 216: 346–355. https://doi.org/10.1016/j.procs.2022.12.145.
  56. Durden JM. Environmental Management Using a Digital Twin. Environmental Science & Policy 2025; 164: 104018. https://doi.org/10.1016/j.envsci.2025.104018.
  57. Giannarakis G, Sitokonstantinou V, Lorilla RS, et al. Towards Assessing Agricultural Land Suitability with Causal Machine Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 1442–1452.
  58. Valverde A, Roger J, Gorroño J, et al. Detecting Methane Emissions from Palm Oil Mills with Airborne and Spaceborne Imaging Spectrometers. Environmental Research Letters 2024; 19(12): 124003. https://doi.org/10.1088/1748-9326/ac01ab.
  59. Vaughan A, Mateo-Garcia G, Irakulis-Loitxate I, et al. AI for Operational Methane Emitter Monitoring from Space. arXiv 2024; arXiv240804745.
  60. Ruzlan KAC, bin Bakar SA, Manan C, et al. Weed Control Efficiency of Unmanned Aerial Vehicle Spray in Replanting Oil Palm Plantation Areas. Weed Science 2025; 73: e37. https://doi.org/10.1017/wsc.2024.91.
  61. Ashari S, Yanris GJ, Purnama I. Oil Palm Fruit Ripeness Detection Using Deep Learning. Sinkron: Jurnal Dan Penelitian Teknik Informatika 2022; 6(2): 649–656. https://doi.org/10.33395/sinkron.v7i2.11420.
  62. Nath G, Harfouche A, Coursey A, et al. Integration of a Machine Learning Model into a Decision Support Tool to Predict Absenteeism at Work of Prospective Employees. arXiv 2022; arXiv220203577.
  63. Febrianda R. Mobile App Technology Adoption in Indonesia’s Agricultural Sector: An Analysis of Empirical View from Public R&D Agency. STI Policy and Management Journal 2021; 6(1): 31–40. https://doi.org/10.14203/STIPM.2021.302.
  64. Akhtar MN, Ansari E, Alhady SSN, et al. Leveraging on Advanced Remote Sensing- and Artificial Intelligence-Based Technologies to Manage Palm Oil Plantation for Current Global Scenario: A Review. Agriculture 2023; 13(2): 504. https://doi.org/10.3390/agriculture13020504.
  65. Sokoastri V, Setiadi D, Hakim AR, et al. Smallholders Oil Palm: Problems and Solutions. Sodality: Jurnal Sosiologi Pedesaan 2019; 7(3): 182–194. https://doi.org/10.22500/sodality.v7i3.27221.
  66. Witjaksono J, Djaenudin D, Fery Purba S, et al. Corporate Farming Model for Sustainable Supply Chain Crude Palm Oil of Independent Smallholder Farmers. Frontiers in Sustainable Food Systems 2024; 8: 1418732. https://doi.org/10.3389/fsufs.2024.1418732.
  67. Kurniawan R, Samsuryadi S, Mohamad FS, et al. Advancing palm Oil Fruit Ripeness Classification Using Transfer Learning in Deep Neural Networks. Bulletin of Electrical Engineering and Informatics 2025; 14(2): 1126–1137. https://doi.org/10.11591/eei.v14i2.8651.
  68. Wu J, Tao R, Zhao P, et al. Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 1712–1720. https://doi.org/10.1109/CVPR52688.2022.00176.
  69. Khairunniza-Bejo S, Shahibullah MS, Azmi ANN, et al. Non-Destructive Detection of Asymptomatic Ganoderma Boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine. Applied Sciences 2021; 11(22): 10878. https://doi.org/10.3390/app112210878.
  70. Chen HY, Sharma K, Sharma C, et al. Integrating Explainable Artificial Intelligence and Blockchain to Smart Agriculture: Research Prospects for Decision Making and Improved Security. Smart Agricultural Technology 2023; 6: 100350. https://doi.org/10.1016/j.atech.2023.100350.
  71. Eyo-Udo N. Leveraging Artificial Intelligence for Enhanced Supply Chain Optimization. Open Access Research Journal of Multidisciplinary Studies 2024; 7(2): 1–15. https://doi.org/10.53022/oarjms.2024.7.2.0044.
  72. Gu Z, Zeng M. The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives. Sustainability 2024; 16(1): 274. https://doi.org/10.3390/su16010274.
  73. de Koning K, Broekhuijsen J, Kühn I, et al. Digital Twins: Dynamic Model-Data Fusion for Ecology. Trends in Ecology & Evolution 2023; 38(10): 916–926. https://doi.org/10.1016/j.tree.2023.04.010.
  74. Ghassemi Nejad J, Ju MS, Jo JH, et al. Advances in Methane Emission Estimation in Livestock: A Review of Data Collection Methods, Model Development and the Role of AI Technologies. Animals 2024; 14(3): 435. https://doi.org/10.3390/ani14030435.
  75. Azghadi MR, Olsen A, Wood J, et al. Precise Robotic Weed Spot-Spraying for Reduced Herbicide Usage and Improved Environmental Outcomes—A Real-World Case Study. arXiv 2024; arXiv240113931.
  76. Madanchian M, Taherdoost H, Mohamed N. AI-Based Human Resource Management Tools and Techniques: A Systematic Literature Review. Procedia Computer Science 2023; 229: 367–377. https://doi.org/10.1016/j.procs.2023.09.029.
  77. Ghazal S, Munir A, Qureshi WS. Computer Vision in Smart Agriculture and Precision Farming: Techniques and Applications. In Artificial Intelligence in Agriculture; Elsevier: Amsterdam, The Netherlands, 2024. https://doi.org/10.1016/B978-0-12-822748-9.00012-0.
  78. Yuan Y, Sun Y. Practices, Challenges, and Future of Digital Transformation in Smallholder Agriculture: Insights from a Literature Review. Agriculture 2024; 14(12). https://doi.org/10.3390/agriculture14122193.
  79. Rafi MSM, Behjati M, Rafsanjani AS. Reliable and Cost-Efficient IoT Connectivity for Smart Agriculture: A Comparative Study of LPWAN, 5G, and Hybrid Connectivity Models. arXiv 2025; arXiv250311162.
  80. Sukiyono K, Romdhon MM, Mulyasari G, et al. The Contribution of Oil Palm Smallholders Farms to the Implementation of the Sustainable Development Goals-Measurement Attempt. Sustainability 2022; 14(11): 6843. https://doi.org/10.3390/su14116843.

Supporting Agencies

  1. Funding: This research received no external funding.