RENEWABLE ENERGY AND AI IMPLEMENTATION AS SUSTAINABLE STRATEGY FOR AGRICULTURE RESILIENCE TO CLIMATE CHANGE

Climate changes and ecological sustainability in agriculture and food production in Serbia, the region and Southeastern Europe : proceedings, (pp. 90-97)

AUTHOR(S) / АУТОР(И): Ivan Stevović1, Sabahudin Hadrović2, Bratislav Ćirković3

1Innovation center of the Faculty of Mechanical engineering, Belgrade, Serbia; 2Institute of forestry, Belgrade, Republic of Serbia; 3University of Pristina temporary settled in Kosovska Mitrovica, Faculty of Agriculture, Lešak, Kosovo and Metohija, Serbia

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DOI: 10.46793/MAK2025.090S

ABSTRACT / САЖЕТАК:

Climate change has a major impact on all human activities. One of the most important is agriculture from the point of view of food production, as the starting point of survival on the globe. The negative consequences of climate change are reflected in increasingly pronounced droughts and catastrophic floods, and are also reflected in the increase in the frequency of these extremes. Multidisciplinary teams of engineers and scientists are engaged in the development of sustainable strategies, with the aim of increasing the resilience of agriculture to climate change. The application of renewable energy sources and artificial intelligence plays a significant role in the management of sustainable strategies for the protection and improvement of smart agriculture. Wind turbines and photovoltaic panels raised above agricultural land at an appropriate distance form a synergy of agricultural development and renewable energy. Their implementation in itself reduces the emission of greenhouse gases and contributes to mitigating climate change. At the same time, this energy can be used both in the system and locally for pumping water for irrigation. Hydropower and the construction of water acumulations also provide increased opportunities for solving droughts and floods and thereby improving and stabilizing agricultural production and higher resilience of agriculture to climate change. Agricultural residues can be used as a resource for renewable bioenergy. Artificial intelligence, supported by contemporary solutions of sensor technology, helps us in the optimal management of all these complex processes. This research also contains a positive case studies from international practice on the implementation of renewable energy sources and artificial intelligence and their concrete contribution to increasing the resilience of agriculture to climate change.

KEYWORDS / КЉУЧНЕ РЕЧИ:

Sustainability, Climate Change, Smart Agriculture, Renewable Energy, Artificial Intelligence

ACKNOWLEDGEMENT / ПРОЈЕКАТ:

The results presented in this manuscript are supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, Contract 451-03-66/2024-03/200213 dated 05.02.2024

REFERENCES / ЛИТЕРАТУРА

  • Arockia Doss, A.S., Jeyabalan, A., Rekha Borah, P., Lingampally, P.K., Schilberg, D. (2024). Advancements in Agricultural Automation: A Comprehensive Review of Artificial Intelligence and Humanoid Robotics in Farming. International Journal of Humanoid Robotics, 21(4), 2350012. doi.org/10.1142/S0219843623500123
  • Arun, D.P., Mishra, A. (2024). Enabling Digital Platforms: Toward Smart Agriculture. In Artificial Intelligence Techniques in Smart Agriculture. Singapore: Springer Nature Singapore, 237-251.
  • Attri, I., Awasthi, L.K., Sharma, T.P. (2024). Machine learning in agriculture: a review of crop management applications. Multimedia Tools and Applications, 83(5), 12875-12915
  • Awokuse, T., Lim, S., Santeramo, F., Steinbach, S. (2024). Robust policy frameworks for strengthening the resilience and sustainability of agri-food global value chains. Food Policy, 127, 102714
  • Boix-Fayos, C., de Vente, J. (2023). Challenges and potential pathways towards sustainable agriculture within the European Green Deal. Agricultural Systems, 207, 103634
  • Borusevich, A., Pisarek, L. (2024). Impact of small wind turbines on the surrounding and agricultural environment. Український журнал природничих наук, (9), 140-149.
  • Das, S. (2024). Transforming Agriculture: Harnessing Robotics and Drones for Sustainable Farming Solution. Journal of Experimental Agriculture International, 46(7), 219-231.
  • Domínguez, A.G., Roig-Tierno, N., Chaparro-Banegas, N., García-Álvarez-Coque, J.M. (2024). Natural language processing of social network data for the evaluation of agricultural and rural policies. Journal of Rural Studies, 109, 103341
  • Elufioye, O.A., Ike, C.U., Odeyemi, O., Usman, F.O., Mhlongo, N.Z. (2024). 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, 5(2), 473-497.
  • Fuentes-Peñailillo, F., Gutter, K., Vega, R., Silva, G.C. (2024). Transformative technologies in digital agriculture: Leveraging Internet of Things, remote sensing, and artificial intelligence for smart crop management. Journal of Sensor and Actuator Networks, 13(4), 39.
  • Fuentes-Peñailillo, F., Ortega-Farías, S., Acevedo-Opazo, C., Rivera, M., Araya-Alman, M. (2023). A Smart Crop Water Stress Index-Based IoT Solution for Precision Irrigation of Wine Grape. Sensors, 24(1), 25.
  • Haloui, D., Oufaska, K., Oudani, M., El Yassini, K. (2024). Bridging Industry 5.0 and Agriculture 5.0: Historical Perspectives, Opportunities, and Future Perspectives. Sustainability, 16(9), 3507
  • Islam, M.H., Anam, M.Z., Hoque, M.R., Nishat, M., Bari, A.M. (2024). Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability. Journal of Economy and Technology, 2, 278-295.
  • Jeffrey, L., Bommu, R. (2024). Innovative AI Solutions for Agriculture: Enhancing CropManagement and Yield. International Journal of Advanced Engineering Technologies and Innovations, 1(3), 203-221.
  • Kassem, Y., Camur, H., Ghoshouni, E.G. (2024). Assessment of a Hybrid (Wind-Solar) System at High-Altitude Agriculture Regions for achieving Sustainable Development Goals. Engineering, Technology & Applied Science Research, 14(1), 12595-12607
  • Kumar, S., Singh, D., Mohan, S., Shakya, A., Diwakar, S.K., Yadav, V.K. (2024). Genetically Modified Crops: Resistant to Pest and Environmental Stress: A Review. Journal of Advanced Zoology, 45(2).
  • Luo, J., Luo, Z., Li, W., Shi, W., Sui, X. (2024). The Early Effects of an Agrivoltaic System within a Different Crop Cultivation on Soil Quality in Dry-Hot Valley Eco-Fragile Areas. Agronomy, 14(3), 584.
  • Morchid, A., El Alami, R., Raezah, A.A., Sabbar, Y. (2024). Applications of internet of things (IoT) and sensors technology to increase food security and agricultural Sustainability: Benefits and challenges. Ain Shams Engineering Journal, 15(3), 102509
  • Saleem, A., Anwar, S., Nawaz, T., Fahad, S., Saud, S., Ur Rahman, T., Nawaz, T. (2024). Securing a sustainable future: the climate change threat to agriculture, food security, and sustainable development goals. Journal of Umm Al-Qura University for Applied Sciences, 1-17.
  • Schmitt, R.J.P., Rosa, L. (2024). Dams for hydropower and irrigation: Trends, challenges, and alternatives. Renewable and Sustainable Energy Reviews, 199, 114439.
  • Taseer, A., Han, X. (2024). Advancements in variable rate spraying for precise spray requirements in precision agriculture using Unmanned aerial spraying Systems: A review. Computers and Electronics in Agriculture, 219, 108841
  • Toplicean, I.M., Datcu, A.D. (2024). An Overview on Bioeconomy in Agricultural Sector, Biomass Production, Recycling Methods, and Circular Economy Considerations. Agriculture, 14(7), 1143
  • Vellingiri, A., Kokila, R., Nisha, P., Kumar, M., Chinnusamy, S., Boopathi, S. (2025). Harnessing GPS, Sensors, and Drones to Minimize Environmental Impact: Precision Agriculture. In Designing Sustainable Internet of Things Solutions for Smart Industries (pp. 77-108). IGI Global.
  • Wang, J., Sun, X., Zhang, S., Zhang, X. (2024a). Does Addressing Rural Energy Poverty Contribute to Achieving Sustainable Agricultural Development? Agriculture, 14(6), p. 795.
  • Wang, J., Wang, Y., Li, G., Qi, Z. (2024b). Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications. Agronomy, 14(9), 1975.
  • Zhou, Y. (2024). Technological Innovation and Significance of Vertical Farming System in High-Density Urban Areas. In E3S Web of Conferences (Vol. 579, p. 03001). EDP Sciences.