THE USE OF INFORMATION TECHNOLOGIES AND IMPLEMENTING BIG DATA CONCEPT, DRONES, AND ARTIFICIAL INTELLIGENCE IN THE AGRICULTURE - PERCEPTION OF SMALL FARMERS IN SERBIA

Tatjana Ilić-Kosanović; Brankica  Pažun; Zlatko  Langović; Damir  Ilić

doi:10.59267/ekoPolj25041293I

Authors

Tatjana Ilić-Kosanović Associate professor, University Union - Nikola Tesla, Cara Dušana 62-64, Belgrade, Serbia
Brankica Pažun University Union - Nikola Tesla;, School of Engineering Management, Bulevar Vojvode Mišića, 43, Belgrade, Serbia
Zlatko Langović University of Kragujevac, Faculty of Hotel Management and Tourism in Vrnjačka Banja, Vojvođanska 5a, Vrnjačka banja, Serbia
Damir Ilić University Union - Nikola Tesla;, School of Engineering Management, Bulevar Vojvode Mišića, 43, Belgrade, Serbia

DOI:

https://doi.org/10.59267/ekoPolj25041293I

Keywords:

Artificial intelligence, small farmers, information communication technologies, education, Serbia

Abstract

This study explores the attitudes and readiness of small farmers in the Republic of Serbia toward digital transformation in agricultural production, with emphasis on the application of Big Data, drone technology, and artificial intelligence. The research implemented a quantitative survey, collecting data from 437 participants across three regions of Serbia. Descriptive statistics and Spearman’s rank correlation analysis were used to examine the correlation of demographic factors, including age, gender, education level, and geographic location, with the perceptions on using information technologies in agriculture. Results present significant interest in digital tools that support productivity and sustainability, despite limited practical experience and low levels of digital literacy. Statistically significant correlations were identified between age, education level, and geography in shaping openness toward technological adoption, while gender showed no significant correlation. Younger and more educated respondents consistently expressed stronger support for using advanced technologies, underscoring the importance of strategic government awareness programs and training initiatives.

Downloads

Download data is not yet available.

References

Abbas, A., Zhang, Z., Zheng, H., Alami, M. M., Alrefaei, A. F., Abbas, Q., ... & Zhou, L. (2023). Drones in plant disease assessment, efficient monitoring, and detection: A way forward to smart agriculture. Agronomy, 13(6), 1524. https://doi.org/10.3390/agronomy13061524.

Ahmed, N., & Shakoor, N. (2025). Advancing agriculture through IoT, Big Data, and AI: A review of smart technologies enabling sustainability. Smart Agricultural Technology. Elsevier B.V. https://doi.org/10.1016/j.atech.2025.100848.

Aijaz, N., Lan, H., Raza, T., Yaqub, M., Iqbal, R., & Pathan, M. S. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research. Elsevier B.V. https://doi.org/10.1016/j.jafr.2025.101762.

Alaoui, M. E., Amraoui, K. E., Masmoudi, L., Ettouhami, A., & Rouchdi, M. (2024). Unleashing the potential of IoT, Artificial Intelligence, and UAVs in contemporary agriculture: A comprehensive review. Journal of Terramechanics. Elsevier Ltd. https://doi.org/10.1016/j.jterra.2024.100986.

Arza-García, M., & Burgess, A. J. (2023). Drones in the Sky: Towards a More Sustainable Agriculture. Agriculture, 13(1), 84. https://doi.org/10.3390/agriculture13010084.

Backman, J., Koistinen, M., & Ronkainen, A. (2023). Agricultural process data as a source for knowledge: Perspective on artificial intelligence. Smart Agricultural Technology, 5. https://doi.org/10.1016/j.atech.2023.100254.

Bešić, C., Bogetić, S., Bakator, M., & Petrevska, I. (2024). The impact of sustainability, digital technologies, and employee knowledge on the competitiveness of personalized tourist offer. Hotel and Tourism Management, 12(1). https://doi.org/10.5937/menhottur2400010B

Bešić, C., Ćoćaklo, D., Bakator, M., Vidas-Bubanja, M., & Stanisavljev, S. (2025). Agriculture 5.0 potential and the application of advanced technologies in Serbia. Ekonomika Poljoprivrede, 72(2), 599–616. https://doi.org/10.59267/ekoPolj2502599B

Boros, A., Szólik, E., Desalegn, G., & Tőzsér, D. (2025). A Systematic Review of Opportunities and Limitations of Innovative Practices in Sustainable Agriculture. Agronomy, 15(1), 76. https://doi.org/10.3390/agronomy15010076.

Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., … Goudos, S. K. (2022). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things (Netherlands). Elsevier B.V. https://doi.org/10.1016/j.iot.2020.100187.

Bu, F., & Wang, X. (2019). A smart agriculture IoT system based on deep reinforcement learning. Future Generation Computer Systems, 99, 500–507. https://doi.org/10.1016/j.future.2019.04.041.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ. Erlbaum.

Costa, L., Nunes, L., & Ampatzidis, Y. (2020). A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Computers and Electronics in Agriculture 172, 105334. https://doi.org/10.1016/j.compag.2020.105334.

del Cerro, J., Cruz Ulloa, C., Barrientos, A., & de León Rivas, J. (2021). Unmanned Aerial Vehicles in Agriculture: A Survey. Agronomy, 11(2), 203. https://doi.org/10.3390/agronomy11020203.

Delgado, J. A., Short Jr, N. M., Roberts, D. P., & Vandenberg, B. (2019). Big data analysis for sustainable agriculture on a geospatial cloud framework. Frontiers in Sustainable Food Systems, 3, 54. doi: 10.3389/fsufs.2019.00054.

Dimitrijević, M., Ristić, L., & Despotović, D. (2021). Rural development of regions of the Republic of Serbia in terms of employment and sources of income. The Annals of the Faculty of Economics in Subotica, 57(46), 131–148. https://doi.org/10.5937/AnEkSub2146131D.

Drăgoi, M. C., Andrei, J. V., Mieilă, M., Panait, M., Dobrotă, C. E., & Lădaru, R. G. (2018). Food safety and security in Romania–an econometric analysis in the context of national agricultural paradigm transformation. Amfiteatru Economic, 20(47), 134-150.

Đurić, K., Cvijanović, D., Prodanović, R., Čavlin, M., Kuzman, B., & Lukač Bulatović, M. (2019). Serbian agriculture policy: Economic analysis using the PSE approach. Sustainability, 11(2), 309. https://doi.org/10.3390/su11020309

Erokhin, V., Tianming, G., Chivu, L., & Andrei, J. V. (2022). Food security in a food self-sufficient economy: A review of China’s ongoing transition to a zero hunger state. Agricultural Economics/Zemědělská Ekonomika, 68(12), 476–487.

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. https://doi.org/10.3390/jsan13040039.

Gebresenbet, G., Bosona, T., Patterson, D., Persson, H., Fischer, B., Mandaluniz, N.,… Nasirahmadi, A. (2023). A concept for application of integrated digital technologies to enhance future smart agricultural systems. Smart Agricultural Technology, 5. https://doi.org/10.1016/j.atech.2023.100255.

Grujić Vučkovski, B., & Subić, J. (2024). Digitalization in agriculture and application in Serbia. In Proceedings of the Second International Scientific Conference “Challenges of Digitalization in the Business World” (pp. 50–62). Alfa BK University.

Guebsi, R., Mami, S., & Chokmani, K. (2024). Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges. Drones, 8(11), 686. https://doi.org/10.3390/drones8110686.

Ilic-Kosanovic, T., Pazun, B., Langovic, Z., & Tomic, S. (2019). Perception of Small Farmers in Serbia Regarding the Use of ICT and Possibilities of Organic Agriculture. Ekonomika Poljoprivrede, 66(4), 989–1001. https://doi.org/10.5937/ekoPolj1904989I.

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111. https://doi.org/10.1142/S2424862221300040.

Javaid, M., Haleem, I., Haleem Khan, A., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem 2: 15–30. doi:10.1016/j.aac.2022.10.001.

Jurjević, Ž., Bogićević, I., Đokić, D., & Matkovski, B. (2019). Information technology as a factor of sustainable development of Serbian agriculture. Strategic Management, 24(1), 41-46. https://doi.org/10.5937/StraMan1901041J

Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of Agriculture 4.0: How do we manage diversity and responsibility in food system transition pathways?. Global Food Security, 24. https://doi.org/10.1016/j.gfs.2019.100347.

Kljajić, N., Paraušić, V., & Stanković, Z. (2024). Economic Aspects of Digitalization in Serbian Agriculture: Farmers’ Attitudes. Ekonomika Poljoprivrede, 71(3), 943– 956. https://doi.org/10.59267/ekoPolj2403943K.

Langović, Z., Pažun, B., Grujčić, Ž., Nikolić, M., Langović-Milićević, A, Ugrinov, D. (2025). MCDM Approach Combining DEA and AHP Methods in Sustainable Tourism: Case of Serbia, Journal of Scientific&Industrial Research. 84(2), 183- 195 https://doi.org/10.56042/jsir.v84i02.8163

Linaza, M. T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., G. Olaizola, I., Barriguinha, A., Moysiadis, T., & Lucat, L. (2021). DataDriven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy, 11(6), 1227. https://doi.org/10.3390/agronomy11061227.

Merz, M., Pedro, D., Skliros, V., Bergenhem, C., Himanka, M., Houge, T., MatosCarvalho, J. P., Lundkvist, H., Cürüklü, B., Hamrén, R., Ameri, A. E., Ahlberg, C., & Johansen, G. (2022). Autonomous UAS-Based Agriculture Applications: General Overview and Relevant European Case Studies. Drones, 6(5), 128. https://doi.org/10.3390/drones6050128.

Meshram, V., Patil, K., Meshram, V., Hanchate, D., & Ramkteke, S. D. (2021). Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences, 1, 100010.

Michels, M., von Hobe, C. F., Weller von Ahlefeld, P. J. et al. (2021). The adoption of drones in German agriculture: a structural equation model. Precision Agric 22, 1728–1748. https://doi.org/10.1007/s11119-021-09809-8.

Milačić, D. (2024). Strategijski menadžment kao instrument razvoja održivog turizma u Srbiji. Održivi razvoj, 6(2), 7-22. https://doi.org/10.5937/OdrRaz2402007M

Mohr, S., & Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816-1844. https://doi.org/10.1007/s11119-021-09814-x.

Municipality Vrbas (2022). Opština Vrbas, naseljena mesta. Offciial web site of Vrbas municipality. Retrieved on February 18, 2022 from https://www.vrbas.net/opstina-vrbas/naseljena-mesta

Näsi, R., Mikkola, H., Honkavaara, E., Koivumäki, N., Oliveira, R. A., Peltonen-Sainio, P., ... & Alakukku, L. (2023). Can basic soil quality indicators and topography explain the spatial variability in agricultural fields observed from drone orthomosaics?. Agronomy, 13(3), 669. https://doi.org/10.3390/agronomy13030669.

Oliveira, R. C. d., & Silva, R. D. d. S. e. (2023). Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends. Applied Sciences, 13(13), 7405.https://doi.org/10.3390/app13137405.

Pantović D., Pantić, N. & Milojević I. (2022), Role of the tourism 4.0 in Visegrad countries, Monograph: Possibilities and barriers for Industry 4.0 implementation in SMEs in V4 countries and Serbia, Technical Faculty in Bor, 288-303.

Partel, V., Costa, L., & Ampatzidis, Y. (2021). Smart tree crop sprayer utilizing sensor fusion and artificial intelligence. Computers and Electronics in Agriculture, 191, 106556. https://doi.org/10.1016/j.compag.2021.106556.

Paunović, M., Štrbac, D., & Živković, L. (2024). Gender Perspectives of Twin Transition in Agriculture and Food Sector Companies: Empirical Evidence from Serbia. Ekonomika Poljoprivrede, 71(3), 895–908. https://doi.org/10.59267/ekoPolj2403895P.

Pažun, B, Langović, Z, Stojanović, V.S, Langović-Milićević, A, Božović, I. (2025). The Influence of Information and Communication Technology on Economic Growth in Europe. Journal of Knowledge Economy, 1-29, https://doi.org/10.1007/s13132-024-02576-7

Petkovic, S., Petkovic, D., & Petkovic, A. (2017). IoT devices VS. drones for data collection in agriculture. DAAAM International Scientific Book, 16, 63-80. doi: 10.2507/daaam.scibook.2017.06.

Phang, S. K., Chiang, T. H. A., Happonen, A., & Chang, M. M. L. (2023). From satellite to UAV-based remote sensing: A review on precision agriculture. Ieee Access, 11, 127057-127076. https://doi.org/10.1109/ACCESS.2023.3330886.

Rađenović, Ž., Krstić, B., & Marković, M. (2020). Smart Farming in Agricultural Industry: Mobile Technology Perspective. Ekonomika Poljoprivrede, 67(3), 925–938. https://doi.org/10.5937/ekoPolj2003925R

Radic, V. N., Radić, N. V., & Cogoljević, V. D. (2022). New Technologies as a Driver of Change in the Agricultural Sector. Ekonomika Poljoprivrede, 69(1), 147–162. https://doi.org/10.5937/ekoPolj2201147R.

Rejeb, A., Abdollahi, A., Rejeb, K., & Treiblmaier, H. (2022). Drones in agriculture: A review and bibliometric analysis. Computers and Electronics in Agriculture. Elsevier B.V. https://doi.org/10.1016/j.compag.2022.107017.

Republički zavod za statistiku (2011). Popis 2011, Uporedni broj stanovnika 1948- 2011. Knjiga 20. Republički zavod za statistiku. Beograd, Srbija.

Roslim, M. H. M., Juraimi, A. S., Che’Ya, N. N., Sulaiman, N., Manaf, M. N. H. A., Ramli, Z., & Motmainna, M. (2021). Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review. Agronomy, 11(9), 1809. https://doi.org/10.3390/agronomy11091809.

Schaefer, L. (2023). An Emerging Era of Artificial Intelligence Research in Agriculture. Journal of Robotics Spectrum, 1, 036-046. doi: https://doi.org/10.53759/9852/JRS202301004.

Skupština grada Kraljeva. (2017). Osnovne karakteristike. Retrieved from: www.kraljevo.rs/wp-content/uploads/2017/11/01.Osnovne-karakteristike.pdf [in English: Kraljevo Assembly. (2017). Basic information]

Spanaki, K., Karafili, E., Sivarajah, U., Despoudi, S., & Irani, Z. (2022). Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations. Production Planning & Control, 33(16), 1498-1516. https://doi.org/10.1080/09537287.2021.1882688.

Stojiljković, M., Raičević, J., & Djurković, M. (2025). Harmonization of the agricultural policy of the Republic of Serbia with the agricultural policy of the European Union. Ekonomika Poljoprivrede, 72(2), 741–755. https://doi.org/10.59267/ekoPolj2502741S.

Stojković, A., & Kocić, S. (2024). Analiza stresnih poremećaja i javnog nastupa. Finansijski savetnik, 29(1), 27-38.

Škrbić, S., & Obrić, B. (2024). Budžetsko-pravna analiza finansiranja rashoda odbrane. Revija prava javnog sektora, 4(1), 7-22.

Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial intelligence in agriculture, 4, 58-73. https://doi.org/10.1016/j.aiia.2020.04.002.

Uzhinskiy, A. (2023). Advanced Technologies and Artificial Intelligence in Agriculture. AppliedMath, 3(4), 799-813. https://doi.org/10.3390/appliedmath3040043.

van der Merwe, D., Burchfield, D. R., Witt, T. D., Price, K. P., & Sharda, A. (2020). Drones in agriculture. In Advances in Agronomy (Vol. 162, pp. 1–30). Academic Press Inc. https://doi.org/10.1016/bs.agron.2020.03.001.

Vapa Tankosić, J., Mirjanić, B., Prodanović, R., Lekić, S., & Carić, B. (2024). Digitalization in agricultural sector: Agriculture 4.0 for sustainable agriculture. Journal of Agronomy, Technology and Engineering Management, 7(1), 1036– 1042.

Vukadinovic, S., Jesic, J. S., Okanovic, A., & Lovre, I. (2022). Digital Agriculture - The Case of Autonomous Province of Vojvodina. Ekonomika Poljoprivrede, 69(1), 133–145. https://doi.org/10.5937/ekoPolj2201133V.

Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A., & Lynch, I. (2021). Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nature Plants, 7(7), 864-876. doi: 10.1038/s41477-021-00946-6.