Pemanfaatan Artificial Intelligence Dalam Manajemen Rantai Pasok Produk Pertanian: Tinjauan Literatur Sistematik
DOI:
https://doi.org/10.29244/fagb.15.2.227-242Keywords:
agricultural product, artificial intelligence, deep learning, machine learning, supply chainAbstract
The agricultural product supply chain frequently faces challenges, including fluctuations in demand, climate change, and the perishable nature of products, which can result in inefficiencies and losses. These issues require technology to optimize supply chain performance, one of which is through the use of Artificial Intelligence (AI). This study aims to identify the types of AI commonly used, their applications across various stages of the supply chain, their role in enhancing efficiency, and the challenges associated with their implementation. The method used is a Systematic Literature Review (SLR) based on 21 scientific articles from 2015 to 2025 sourced from the Scopus database. Articles were selected based on criteria including journals and proceedings, open access, and relevance to AI applications in agricultural product supply chains. The research results indicate that machine learning and deep learning are the most widely used types of AI, particularly for crop yield prediction, plant disease detection, product quality classification, and logistics management. AI has been applied across various stages of the supply chain, from cultivation, processing, to distribution. AI has proven to enhance efficiency, real-time monitoring, and decision-making. However, its implementation still faces challenges such as limited quality data, inadequate infrastructure, high implementation costs, and low human resource capacity. Therefore, the utilization of AI in the agricultural product supply chain requires collaboration between the government, academia, industry, and farmers. On the other hand, regulations and policies supporting AI adoption also need further review to ensure this technology can be widely and sustainably implemented.
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References
Adriana, S. N., Sadeli, A. H., Sulistyodewi, & Syamsiyah, N. (2023). Minat Konsumen Terhadap Produk Pertanian. Prospek Agribisnis, 1(1), 331–344. https://doi.org/https://jurnal.unpad.ac.id/prospekagribisnis/article/view/53400
Alagalla, A. W. H. P., & Weerasinghe, L. (2023). Best Profitable Crops Prediction with Profit, Cost, and Farmland Optimization using Machine Learning. International Journal of Computer Information Systems and Industrial Management Applications, 15(2023), 550–565. https://doi.org/https://cspub-ijcisim.org/index.php/ijcisim/article/view/575
Alberto, F., & Takaya, R. (2024). Analisis Kritis Tentang Pengelolaan Rantai Pasokan Dalam Konteks Globalisasi. Jurnal Ilmiah Multidisiplin Terpadu, 8(7), 177–183. https://doi.org/https://sejurnal.com/pub/index.php/jimt/article/view/3684
Barricelli, B. R., Cassano, F., Fogli, D., & Piccinno, A. (2019). End-User Development, End-User Programming And End-User Software Engineering: A Systematic Mapping Study. Journal of Systems and Software, 149, 101–137. https://doi.org/https://doi.org/10.1016/j.jss.2018.11.041
Beddows, M., & Leontidis, G. (2024). A Multi-Farm Global-to-Local Expert-Informed Machine Learning System for Strawberry Yield Forecasting. Agriculture (Switzerland), 14(6). https://doi.org/10.3390/agriculture14060883
Boell, Sebastian K, & Cecez-Kecmanovic, Dubravka. (2015). On being ‘Systematic’ in Literature Reviews in IS. Journal of Information Technology, 30(2), 161–173. https://doi.org/10.1057/jit.2014.26
Bouacida, I., Farou, B., Djakhdjakha, L., Seridi, H., & Kurulay, M. (2024). Innovative Deep Learning Approach For Cross-Crop Plant Disease Detection: A Generalized Method For Identifying Unhealthy Leaves. Information Processing in Agriculture, 12(1), 54–67. https://doi.org/10.1016/j.inpa.2024.03.002
Buyuktepe, O., Catal, C., Kar, G., Bouzembrak, Y., Marvin, H., & Gavai, A. (2023). Food Fraud Detection Using Explainable Artificial Intelligence. Expert Systems, February 2023, 1–20. https://doi.org/10.1111/exsy.13387
Chelliah, B. J., Latchoumi, T. P., & Senthilselvi, A. (2024). Analysis Of Demand Forecasting Of Agriculture Using Machine Learning Algorithm. Environment, Development and Sustainability, 26(1), 1731–1747. https://doi.org/10.1007/s10668-022-02783-9
Colamaria, A., Sacco, M., Parbonetti, G., Blagia, M., Carbone, F., & de Notaris, M. (2021). Isolated Lumbar Intradural Tailgut Cyst: A Case Report And Review Of The Literature. Heliyon, 7(10), e08223. https://doi.org/https://doi.org/10.1016/j.heliyon.2021.e08223
Dhal, S. B., & Kar, D. (2024). Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization. Forecasting, 6(4), 925–951. https://doi.org/10.3390/forecast6040046
Eryc, & Deu, I. (2024). Integrasi Teknologi Digital Dan Ai Dalam Memperkuat Akuntabilitas Pada Operasi Manajemen Rantai Pasokan: Analisis Literatur Sistematis. TEKNIMEDIA, 5(2), 200–211. https://doi.org/https://doi.org/10.46764/teknimedia.v5i2.219
Garillos-Manliguez, C. A., & Chiang, J. Y. (2021). Multimodal Deep Learning And Visible-Light And Hyperspectral Imaging For Fruit Maturity Estimation. Sensors (Switzerland), 21(4), 1–18. https://doi.org/10.3390/s21041288
Gayathiri, B., Brindha, P., Karthika, I., Saranya, E., Rajeshkumar, G., & Rajesh Kanna, P. (2023). Machine Learning based Crop Suitability Prediction and Fertiliser Recommendation System. 2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, July, 1023–1028. https://doi.org/10.1109/ICESC57686.2023.10193542
Halder, S., Islam, M. R., Mamun, Q., Mahmoubi, A., Walsh, P., Islam, M. Z. (2025). A Comprehensive Survey On AI-Enabled Secure Social Industrial Internet Of Things In The Agri-Food Supply Chain. Smart Agricultural Technology, (11). https://doi.org/10.1016/j.atech.2025.100902.
Hande, K. N., & Chandak, M. B. (2024). Optimizing Warehouse Management System With Blockchain And Machine Learning Predictive Data Analytics. International Journal of Informatics and Communication Technology, 13(3), 362–369. https://doi.org/10.11591/ijict.v13i3.pp362-369
Jamshidi, E. J., Yusup, Y., Hooy, C. W., Kamaruddin, M. A., Mat Hassan, H., Muhammad, S. A., Mohd Shafri, H. Z., Then, K. H., Norizan, M. S., & Tan, C. C. (2024). Predicting Oil Palm Yield Using A Comprehensive Agronomy Dataset And 17 Machine Learning And Deep Learning Models. Ecological Informatics, 81(December 2023), 102595. https://doi.org/10.1016/j.ecoinf.2024.102595
Kamilaris, A., Fonts, A., & Prenafeta-Boldύ, F. X. (2019). The Rise Of Blockchain Technology In Agriculture And Food Supply Chains. Trends in Food Science & Technology, 91, 640–652. https://doi.org/https://doi.org/10.1016/j.tifs.2019.07.034
Kraus, S., Breier, M., Lim, W. M., Dabić, M., Kumar, S., Kanbach, D., Mukherjee, D., Corvello, V., Piñeiro-Chousa, J., Liguori, E., Palacios-Marqués, D., Schiavone, F., Ferraris, A., Fernandes, C., & Ferreira, J. J. (2022). Literature Reviews As Independent Studies: Guidelines For Academic Practice. Review of Managerial Science, 16(8), 2577–2595. https://doi.org/10.1007/s11846-022-00588-8
Lailia, N., Rondhi, M., & Soejono, D. (2020). Analisis Rantai Pasok dan Strategi Pengembangan Susu Kambing Pasteurisasi di Goatzilla Farm & Cafe. Forum Agribisnis : Agribusiness Forum, 10(1), 11–26. https://doi.org/10.29244/fagb.10.1.11-26
Le, C. A., Le, C. H., Nguyen, V. D., Zlatov, N., Le, T. H., Nguyen, T. A., Chu, A. M., Mahmud, J., Le, V. D., Nguyen, H. Q., Ramesh, D., Mengistu, S., Behera, A., & Packianather, M. S. (2024). Digital Twins for Real-Time Monitoring and Operation of Coffee Value Chain and Supply Chain. International Journal of Mechatronics and Applied Mechanics, 2024(17), 114–124. https://doi.org/10.17683/ijomam/issue17.13
Lee, D. H., & Park, J. H. (2024). Development of a UAS-Based Multi-Sensor Deep Learning Model for Predicting Napa Cabbage Fresh Weight and Determining Optimal Harvest Time. Remote Sensing, 16(18). https://doi.org/10.3390/rs16183455
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine Learning in Agriculture: A Review. In Sensors (Vol. 18, Issue 8). https://doi.org/10.3390/s18082674
Liu, S., Ampatzidis, Y., Zhou, C., & Lee, W. S. (2025). AI-Driven Time Series Analysis For Predicting Strawberry Weekly Yields Integrating Fruit Monitoring And Weather Data For Optimized Harvest Planning. Computers and Electronics in Agriculture, 233(January), 110212. https://doi.org/10.1016/j.compag.2025.110212
Majeed, Y., Ojo, M. O., & Zahid, A. (2024). Standalone Edge AI-Based Solution For Tomato Diseases Detection. Smart Agricultural Technology, 9(August), 100547. https://doi.org/10.1016/j.atech.2024.100547
Mishra, D., Muduli, K., Raut, R., Narkhede, B. E., Shee, H., & Jana, S. K. (2023). Challenges Facing Artificial Intelligence Adoption during COVID-19 Pandemic: An Investigation into the Agriculture and Agri-Food Supply Chain in India. Sustainability (Switzerland), 15(8). https://doi.org/10.3390/su15086377
Mohammed, S., Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Naumann, F., & Harmouch, H. (2025). The Effects of Data Quality on Machine Learning Performance on Tabular Data. Information Systems, 132, 1–50. https://doi.org/10.1016/j.is.2025.102549
Nebri, M. A., Moussaid, A., & Bouikhalene, B. (2024). Forecasting Livestock Feed Sales Using Machine Learning Techniques: An Analysis Of The Moroccan Market. Indonesian Journal of Electrical Engineering and Computer Science, 35(2), 1139–1150. https://doi.org/10.11591/ijeecs.v35.i2.pp1139-1150
Olawale, R. A., Olawumi, M. A., & Oladapo, B. I. (2025). Sustainable Farming With Machine Learning Solutions For Minimizing Food Waste. Journal of Stored Products Research, 112(March), 102611. https://doi.org/10.1016/j.jspr.2025.102611
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline For Reporting Systematic Reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Putri, F. P., Marimin, & Yuliasih, I. (2020). Peningkatan Efektivitas Dan Efisiensi Manajemen Rantai Pasok Agroindustri Buah: Tinjauan Literatur Dan Riset Selanjutnya. Jurnal Teknologi Industri Pertanian, 30(3), 338–354. https://doi.org/10.24961/j.tek.ind.pert.2020.30.3.338
Qi, B., Ma, X., Yu, J., Dong, J., Cui, X., Zhang, X., Cai, Y., & Zhao, Z. (2025). A Risk Assessment Model For The Entire Rice Processing Chain Based On Kmeans++ And Extreme Learning Machine. Lwt, 223(March). https://doi.org/10.1016/j.lwt.2025.117803
Rejeb, A., Rejeb, K. & Hassoun, A. (2025). The Impact Of Machine Learning Applications In Agricultural Supply Chain: A Topic Modeling-Based Review. Discov Food, 5(141). https://doi.org/10.1007/s44187-025-0041 9-1
Sari, P. N., & Nurmalina, R. (2011). Manajemen Rantai Pasok Pada Rantai Pasok Berjaring Beras Organik. Forum Agribisnis: Agribusiness Forum, 3(2), 111–128. https://doi.org/https://doi.org/10.29244/fagb.3.2.111-128
Serrano-torres, G. J., López-naranjo, A. L., Larrea-cuadrado, P. L., & Mazón-fierro, G. (2025). Transformation of the Dairy Supply Chain Through Artificial Intelligence : A Systematic Review. MDPI Sustainability, 17(3), 1–21. https://doi.org/https://doi.org/10.3390/su17030982
Shahid, M. F., Khanzada, T. J. S., Aslam, M. A., Hussain, S., Baowidan, S. A., & Ashari, R. B. (2024). An Ensemble Deep Learning Models Approach Using Image Analysis For Cotton Crop Classification In AI-Enabled Smart Agriculture. Plant Methods, 20(1), 1–22. https://doi.org/10.1186/s13007-024-01228-w
Shahzadi, G., Jia, F., Chen, L., & John, A. (2024). AI Adoption In Supply Chain Management: A Systematic Literature Review. Journal of Manufacturing Technology Management, 35(6), 1125–1150. https://doi.org/10.1108/JMTM-09-2023-0431
Shamshiri, R. R., Weltzien, C., Hameed, I. A., Yule, I. J., Grift, T. E., Balasundram, S. K., Pitonakova, L., Ahmad, D., & Chowdhary, G. (2018). Research and development in agricultural robotics : A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1–14. https://doi.org/10.25165/j.ijabe.20181104.4278
Suharjito, S., & Marimin, M. (2012). Risks Balancing Model Of Agri-Supply Chain Using Fuzzy Risks Utility Regression. Journal of Theoretical and Applied Information Technology, 41(2), 134–144. https://doi.org/https://www.researchgate.net/publication/250002833_Risks_balancing_model_of_Agri-Supply_chain_using_fuzzy_risks_utility_regression
Sun, Y., Ahmed, I., Alkahtani, M., Khalid, Q. S., & Alqahtani, F. M. (2024). Improved Commodity Supply Chain Performance Through AI and Computer Vision Techniques. IEEE Access, 12, 24116–24132. https://doi.org/10.1109/ACCESS.2024.3361756
Teixeira, A. R., & Ferreira, J. V. (2025). Intelligent Supply Chain Management : A Systematic Literature Review on Artificial Intelligence Contributions. MDPI Information, 16(5), 1–40. https://doi.org/https://doi.org/10.3390/info16050399
Thomas, A., Sylaja, M. M., & Kurian, J. (2024). Refinement of Chipless RFID Tags across Multiple Positions for Improved Recognition Reliability through Machine Learning Techniques. Progress in Electromagnetics Research C, 150(November), 57–68. https://doi.org/10.2528/PIERC24092505
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial Intelligence In Supply Chain Management: A Systematic Literature Review. Journal of Business Research, 122, 502–517. https://doi.org/https://doi.org/10.1016/j.jbusres.2020.09.009
Walter, A., Ahsan, K., & Rahman, S. (2025). Application Of Artificial Intelligence In Demand Planning For Supply Chains: A Systematic Literature Review. The International Journal of Logistics Management, 36(3), 672–719. https://doi.org/10.1108/IJLM-02-2024-0120
Wirda, B., Irwandi, P., Muflikh, Y. N., & Nurmalina, R. (2025). The Use Of Supply Chain Operations Reference ( SCOR ) In Measuring The Performance Of Supply Chain Management In The Agribusiness Sector. Journal Agromix, 16(1), 71–91. https://doi.org/https://doi.org/10.35891/agx.v16i1.5742
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/https://doi.org/10.1016/j.agsy.2017.01.023
Yusianto, R., Marimin, Suprihatin, & Hardjomidjojo, H. (2020). Intelligent Spatial Decision Support System Concept In The Potato Agro-Industry Supply Chain. 2020 International Conference on Computer Science and Its Application in Agriculture, ICOSICA 2020, November. https://doi.org/10.1109/ICOSICA49951.2020.9243233
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