The Role of Artificial Intelligence in Advancing Agricultural Technologies within the Russia–China Institutional Partnership
DOI:
https://doi.org/10.17059/ekon.reg.2025-3-14Keywords:
digitalization, artificial intelligence, agriculture 4.0, innovation, digital infrastructure, sustainable development, interstate cooperation, precision farming, agricultural technologies, automation, neural networks, big data, yield forecastingAbstract
Digitalization and artificial intelligence (AI) are playing an increasingly important role in promoting sustainable agricultural development. This is especially clear in the context of institutional cooperation between Russia and China, where agriculture remains a key economic sector. However, despite this significance, the impact of AI and digital technologies on agriculture through interstate collaboration still remains underexplored. This study analyses and forecasts the level of digitalization and AI adoption in the agricultural sectors of Russia and China, while assessing the potential to deepen institutional cooperation. It identifies key factors driving digital transformation in agriculture and projects technology adoption trends through 2035. The methodological framework is grounded in multivariate regression models, which evaluate the influence of infrastructural, economic, social, and technological factors on agricultural digitalization. The analysis is based on 12 indicators of agricultural development in Russia and China from 2013 to 2023, with particular attention to infrastructure and digital metrics. Linear and polynomial regressions were used to model indicator dynamics, and a composite digitalization index was developed using multiple regression. The findings show that China has achieved a high degree of digitalization in agriculture, driven by strong government support, widespread 5G infrastructure, and the integration of IoT technologies. Agricultural automation in China has reached 45%, with projections suggesting an increase to 50% by 2030—potentially boosting productivity by 20–25%. In Russia, the expansion of digital infrastructure lays the groundwork for greater adoption of AI and precision farming technologies, with anticipated productivity gains of 15–20% by 2030. These results may inform national strategies for agricultural digitalization, shape state agricultural policies, and support the implementation of joint Russia–China projects under initiatives such as BRICS+.
References
Abraham, E. R., Mendes dos Reis, J. G., Vendrametto, O., Oliveira Costa Neto, P. L. D., Carlo Toloi, R., Souza, A. E. D., & Oliveira Morais, M. D. (2020). Time series prediction with artificial neural networks: An analysis using Brazilian soybean production. Agriculture, 10 (10), 475. https://doi.org/10.3390/agriculture10100475
Benayad, W., & Khadidja, F. (2024). The application of artificial neural network models to forecast wheat production through time series analysis in key countries. International Journal of Economic Perspectives, 18 (10), 1810–1826. (Date of access: 21.12.2024).
Chirkin, S. O., Kartechina, N. V., & Rubanov, V. A. (2022). Application of artificial intelligence in agriculture. Nauka i obrazovanie [Science and education], 5 (2), 242. https://cyberleninka.ru/article/n/primenenie-iskusstvennogo-intellekta-v-selskom-hozyaystve (Date of access: 21.12.2024). (In Russ.)
Demirel, M. H., Sengul, Z., Baran, M. F., & Gokdogan, O. (2024). Effect of input usage on wheat yield: an application of artificial neural networks (ANN). Journal of Agricultural Science and Technology, 26 (2), 233–245. https://doi.org/10.22034/JAST.26.2.233
García-Navarrete, O. L., Correa-Guimaraes, A., & Navas-Gracia, L. M. (2024). Application of convolutional neural networks in weed detection and identification: A systematic review. Agriculture, 14 (4), 568. https://doi.org/10.3390/agriculture14040568
Gladilina, I. P., Sergeeva, S. A., & Romanova, O. V. (2021). Foreign experience in managing the digital economy. Finansovye rynki i banki [Financial markets and banks], (2), 38–42. (In Russ.)
He, Y., Xu, C., Khanna, N., Boushey, C. J., & Delp, E. J. (2014). Analysis of food images: Features and classification. 2022 IEEE International Conference on Image Processing (ICIP), 2744–2748. https://doi.org/10.1109/icip.2014.7025555
Ji, W., Zhao, D., Cheng, F., Xu, B., Zhang, Y., & Wang, J. (2012). Automatic recognition vision system guided for apple harvesting robot. Computers & Electrical Engineering, 38 (5), 1186–1195. https://doi.org/10.1016/j.compeleceng.2011.11.005
Kaur, K. (2023). Artificial Neural Network Model to Forecast Energy Consumption in Wheat Production in India. Journal of Statistical Theory and Applications, 22 (1), 19–37. https://doi.org/10.1007/s44199-023-00052-w
Kharlamova, T., Ergunova, O., Sharma, N. R., Smirnova, N., & Dudnikov, T. (2024). Model of Strategic Management of National Agribusiness: Conceptual, Theoretical, and Applied aspects. In T. C. Devezas, M. A. Berawi, S. E. Barykin, T. Kudryavtseva (Eds.), Understanding the Digital Transformation of Socio-Economic-Technological Systems. Lecture Notes in Networks and Systems (pp. 353–361). Springer. https://doi.org/10.1007/978-3-031-56677-6_28
Kolmykova, T. S., Shcherbakov, V. N., Tretyakova, I. N., & Sergeeva, V. Yu. (2020). Analytical tool for assessment of the national economy’s readiness for digitalization. Region: sistemy, ekonomika, upravlenie [Region: systems, economics, management], (3(50)), 120–128. (In Russ.)
Kujawa, S., & Niedbała, G. (2021). Artificial Neural Networks in Agriculture. Agriculture, 11 (6), 497. https://doi.org/10.3390/agriculture11060497
Kutyauripo, I., Rushambwa, M., & Chiwazi, L. (2023). Artificial intelligence applications in the agrifood sectors. Journal of Agriculture and Food Research, 11, 100502. https://doi.org/10.1016/j.jafr.2023.100502
Morales, A., & Villalobos, F. J. (2023). Using machine learning for crop yield forecast in the past or the future. Frontiers in Plant Science, 14, 1128388. https://doi.org/10.3389/fpls.2023.1128388
Nandram, B., Berg, E., & Barboza, W. (2013). A Hierarchical Bayesian model for forecasting state-level corn yield. Environmental and Ecological Statistics, 21 (30), 507–530. https://doi.org/10.1007/s10651-013-0266-z
Phiri, S. (2021). Prospects and possibilities of using artificial intelligence in agriculture. Agrarnoe obrazovanie i nauka [Agrarian education and science], (4), 12. https://cyberleninka.ru/article/n/perspektivy-i-vozmozhnosti-ispolzovaniya-iskusstvennogo-intellekta-v-selskom-hozyaystve (Date of access: 12/21/2024). (In Russ.)
Pyankova, S. G., & Ergunova, O. T. (2024). Strategic management of regional agriculture: conceptual, theoretical and applied aspects. Ufimskii gumanitarnyi nauchnyi forum [Ufa Humanitarian Scientific Forum], (1), 209–224. https://doi.org/10.47309/2713–2358-2024-1-209-224 (In Russ.)
Pyankova, S. G., Ergunova, O. T., & Zhukovskii, A. D. (2024). Regional approaches to agricultural enterprises management in the context of the “Foodnet” national food market formation. Ars Administrandi, 16 (2), 353–367. https://doi.org/10.17072/2218–9173-2024-2-353-367 (In Russ.)
Shah, T. M., Nasika, D. P. B., & Otterpohl, R. (2021). Plant and Weed Identifier robot as an agroecological tool using artificial neural networks for image identification. Agriculture, 11 (3), 222. https://doi.org/10.3390/agriculture11030222
Shiri, F. M., Perumal, T., Mustapha, N., & Mohamed, R. (2023). A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU. arXiv (Cornell University). https://doi.org/10.48550/arXiv.2305.17473
Tanima, D., Sharma, N. R., Bohn, R., Ergunova, O., Ryhtik, D., Makarenko, E., & Livintsova, M. (2024). The parallels of food self-sufficiency and hunger in light of sustainable agriculture: A case of the BRICS countries. E3S Web of Conferences, 494, 04043. https://doi.org/10.1051/e3sconf/202449404043
Tsantekidis, A., Passalis, N., & Tefas, A. (2022). Recurrent neural networks. In Deep Learning for Robot Perception and Cognition (pp. 101–115). Academic Press. https://doi.org/10.1016/B978-0-32-385787-1.00010-5
Xu, X., Sun, C., Liu, B., Zhou, Q., Xu, P., Liu, M., Wang, A., Tian, H., Luo, W., & Jiang, Q. (2022). Flesh flavor of red swamp crayfish (Procambarus clarkii Girard, 1852) processing by GS-IMS and electronic tongue is changed by dietary animal and plant protein. Food Chemistry, 373, 131453. https://doi.org/10.1016/j.foodchem.2021.131453
Yan, W. Q. (2023). Convolutional neural networks and recurrent neural networks. In Computational Methods for Deep Learning. Texts in Computer Science (pp. 69–24). Singapore: Springer. https://doi.org/10.1007/978-981-99-4823-9_3
Zaffar, O., Khar, S., Sharma, S., & Singh, JP. (2024). Forecasting of wheat production, productivity and cultivated area in India using artificial neural networks. International Journal of Statistics and Applied Mathematics, 9 (1), 387–394. https://www.mathsjournal.com/pdf/2024/vol9issue1S/PartF/S-9-1-51-239.pdf (Date of access: 20.12.2024).
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