Enhancing agricultural product trade efficiency through machine learning predictions and multi-objective optimization of financial strategies

Main Article Content

Qing Li


agricultural finance, agricultural trade, machine learning, time series analysis, multi-objective optimization


In the context of global economic development and food safety, agricultural trade plays a vital role in linking agricultural production with market demand, and the efficient formulation of agricultural financial strategies is crucial for enhancing trade efficiency. This study employs advanced machine learning technologies to predict and optimize the efficiency of agricultural trade while seeking to balance the interests of various stakeholders in agricultural finance. By integrating quantile factor models, long short-term memory (LSTM) networks, and attention mechanisms, this paper conducts an in-depth analysis and precise prediction of the key factors affecting trade efficiency. This approach effectively addresses the nonlinearity and long-term dependency issues in time-series data, utilizing attention mechanisms to highlight critical information and improve prediction accuracy. Furthermore, this research establishes a multi-objective optimization model to balance the interests of agricultural finance participants, providing a new quantitative tool for formulating agricultural financial strategies aimed at optimizing decisions to enhance economic and social value. This paper offers new perspectives, methods, and empirical support for improving the efficiency of agricultural trade and the formulation of agricultural financial strategies.

Abstract 120 | PDF Downloads 101 HTML Downloads 0 XML Downloads 7


Bai, Y.X., Wang, C., Zeng, M., Chen, Y.H., Wen, H.X., and Nie, P.Y., 2023. Does carbon trading mechanism improve the efficiency of green innovation? Evidence from China. Energy Strategy Reviews 49: 101170. 10.1016/j.esr.2023.101170

Chen, J., 2022. Research on financing mode of new agricultural operators under the background of “Blockchain+Big Data.” In: Proceedings of the 2022 6th Annual International Conference on Data Science and Business Analytics (ICDSBA 2022), 14–16 October 2022, Changsha, China, pp. 219–223. 10.1109/ICDSBA57203.2022.00110

Choudhury, T., Mahdi, H.F., Agarwal, A., Chakraborty, A., Arunachalaeshwaran, V.R., Sarkar, T., et al. 2022. Quality evaluation in guavas using deep learning architectures: an experimental review. In: 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, pp. 1–6. 10.1109/HORA55278.2022.9799824

Deng, X., and Gibson, J., 2019. Improving eco-efficiency for the sustainable agricultural production: a case study in Shandong, China. Technological Forecasting and Social Change 144: 394–400. 10.1016/j.techfore.2018.01.027

Herrera-Franco, G., Sánchez-Arizo, V., Escandón-Panchana, P., Caicedo-Potosí, J., Jaya-Montalvo, M., and Zambrano-Mendoza J., 2023. Analysis of scientific contributions to agricultural development and food security in Ecuador. International Journal of Design & Nature and Ecodynamics (IJDNE) 18(5): 1129–1139. 10.18280/ijdne.180514

Huang, W., Liu, Q., and Hatab, A.A., 2023. Is the technical efficiency green? The environmental efficiency of agricultural production in the MENA region. Journal of Environmental Management 327: 116820. 10.1016/j.jenvman.2022.116820

Li, H., Bi, G., Song, W., and Yuan, X., 2022. Trade credit insurance: insuring strategy of the retailer and the manufacturer. International Journal of Production Research 60(5): 1478–1499. 10.1080/00207543.2020.1861358

Li, W.Q., Han, X.X., Lin, Z.B., and Rahman, A., 2024. Enhanced pest and disease detection in agriculture using deep learning-enabled drones. Acadlore Transactions on AI and Machine Learning (ATAIML) 3(1): 1–10. 10.56578/ataiml030101

Li, J.A., Wang, L., Xie, W.J., and Zhou, W.X., 2023. Impact of shocks to economies on the efficiency and robustness of the international pesticide trade networks. European Physical Journal B: Condensed Matter and Complex Systems 96(2): 25. 10.1140/epjb/s10051-023-00493-3

Mohammadpour, A., Keshtkar, M., Samaei, M.R., Isazadeh, S., and Khaneghah, A.M., 2024a. Assessing water quality index and health risk using deterministic and probabilistic approaches in Darab County, Iran; a machine learning for fluoride prediction. Chemosphere 352: 141284. 10.1016/j.chemosphere.2024.141284

Mohammadpour, A., Samaei, M.R., Baghapour, M.A., Alipour, H., Isazadeh, S., Azhdarpoor, A., et al. 2024b. Nitrate concentrations and health risks in cow milk from Iran: insights from deterministic, probabilistic, and AI modeling. Environmental Pollution 341: 122901. 10.1016/j.envpol.2023.122901

Nisa’ D.P.J.I.N., Darsono, D., and Antriyandarti, E., 2023. Determinants of cocoa bean trade in the international market: gravity model approach. International Journal of Sustainable Development and Planning 18(10): 3347–3356. 10.18280/ijsdp.181035

Qian, X., and Olsen, T.L., 2021. Financial and risk management in agricultural cooperatives with application to the milk industry in New Zealand. International Journal of Production Research 59(19): 5913–5943. 10.1080/00207543.2020.1797204

Sarkar, T., Choudhury, T., Bansal, N., Arunachalaeshwaran, V.R., Khayrullin, M., Shariati, M.A., et al. 2023. Artificial intelligence aided adulteration detection and quantification for red chilli powder. Food Analytical Methods 16(4): 721–748. 10.1007/s12161-023-02445-0

Sarkar, T., Mukherjee, A., Chatterjee, K., Ermolaev, V., Piotrovsky, D., Vlasova, K., et al. 2022. Edge detection aided geometrical shape analysis of Indian gooseberry (Phyllanthusemblica) for freshness classification. Food Analytical Methods 15(6): 1490–1507. 10.1007/s12161-021-02206-x

Seddik, S., Routaib, H., and Elhaddadi, A., 2023. Multi-variable time series decoding with long short-term memory and mixture attention. Acadlore Transactions on Machine Learning Research 2(3): 154–169. 10.56578/ataiml020304

Sun, X., 2022. How to develop agricultural modernization by inclusive finance under modern information technology. In: 2021 International Conference on Big Data Analytics for Cyber-Physical System, pp. 805–813. 10.1007/978-981-16-7469-3_89

Sun, H., 2024. Image processing algorithm design of agricultural internet of things platform based on big data analysis. Journal of Testing and Evaluation 52(3): 20230040. 10.1520/JTE20230040

Tarfi, A., Ismail, I., Idami, Z., and Efendi, E., 2023. Agricultural land redistribution for sustainable peace-building in Aceh, Indonesia. International Journal of Sustainable Development and Planning 18(9): 2923–2931. 10.18280/ijsdp.180930

Wang, P., Wang, Z., and Zhong, H., 2022. The impact of agricultural information system on agricultural product trading efficiency using IoT technology. Mathematical Problems in Engineering 2022: 4061908. 10.1155/2022/4061908

Wu, Y., 2022. The path of agricultural policy finance in smart service for rural revitalization under big data technology. Mobile Information Systems 2022: 9113683. 10.1155/2022/9113683

Yusuf, M.Y., Fadhil, R., Bahri, T.S., Maulana, H., and Firmansyah, J., 2022. Design of Islamic agricultural insurance model: evidence from Indonesia. International Journal of Sustainable Development and Planning 17(8): 2375–2384. 10.18280/ijsdp.170804

Zeng, X., Duan, C., and Wang, R., 2021. Virtual water transfer in Chinese agricultural products trade and its determinants. China Environmental Science 41(2): 983–992. 10.19674/j.cnki.issn1000-6923.2021.0025

Zhao, H.W., Duan, X.F., Qiu, K.X., and Liu, A.L., 2023. Effect of market-oriented reform of rural financial institutions on promoting county economic growth. Journal of Green Economy and Low-Carbon Development (JGELCD) 2(1): 36–48. 10.56578/jgelcd020105