Digital Transformation in Agricultural Structures: Evaluation in Terms of Technological Developments, Practices and Sustainability
DOI:
https://doi.org/10.24925/turjaf.v13is2.3708-3717.8158Keywords:
Internet of Things (IoT), Agriculture 4.0, Digital Agriculture, Precision Livestock Farming (PLF), Smart Greenhouse, Smart Storage SystemsAbstract
Agricultural structures encompass buildings and facilities such as greenhouses, barns, and storage units that are designed to ensure efficient plant and animal production while increasing the controllability of production processes. The inability to regulate environmental conditions in these structures (such as temperature fluctuations) can lead to deteriorating animal health, yield reductions in greenhouses, and increased postharvest losses in storage units. The growing global population, limited availability of natural resources, and the effects of climate change necessitate the adoption of digital solutions in agricultural production. The integration of digital technologies into agricultural structures plays a critical role in enhancing production efficiency, optimizing energy use, improving animal welfare, and ensuring food security. However, this transformation faces significant barriers, including inadequate rural infrastructure, high installation costs, low levels of digital literacy, and limited social acceptance. The widespread implementation of digital agriculture requires not only the development of technological infrastructure but also the education of farmers, the formulation of supportive strategies by policymakers, and strong intersectoral collaboration. Consequently, the digital transformation of agricultural structures stands out as a strategic opportunity in terms of climate change adaptation, sustainable production, and the assurance of global food security. Existing studies on digital transformation in agricultural structures have primarily focused on singular applications, and comprehensive research that holistically addresses technological, socio-economic, environmental, and managerial dimensions has not been conducted. This study was undertaken to systematically examine the digital transformation process within a multidimensional framework, to identify the associated challenges and opportunities, and thereby to provide both sciences in terms of sustainable agricultural production, climate change adaptation, and global food security. Within the scope of this review, the digital transformation process in agricultural structures was examined in detail through a literature survey of international scientific databases such as Scopus, Web of Science, ScienceDirect, ProQuest, ResearchGate, and Google Scholar. Current practices, technical and socio-economic challenges encountered, and future expectations were comprehensively evaluated. Through this analysis, the study demonstrates how digital agricultural technologies were shaped within the framework of Agriculture 4.0, and discussed both their potential within agricultural structures and their impacts on agricultural production processes
References
Anonymous, (2025a April 15). Tarım ve Orman Bakanlığı, Hayvan varlığı istatistikleri. https://istatistik.tarimorman.gov.tr/sayfa/detay/1934
Anonymous, (2025b April 15). Stat Agri, Agricultural Statistics. https://www.statagri.com/ortu-alti-yetistiriciligi/
Arijit, G., Sumit, R., Ashoka, P., Kiran, K., Sabarinathan, B., Saty, S., ... & Kumar, P. S. (2025). Data-driven decision making in agriculture with sensors, satellite imagery and ai analytics by digital farming. Archives of Current Research International, 25(5), 37-52. https://hal.science/hal-05047784v1
Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the agriculture 4.0 landscape-emerging trends, challenges and opportunities. Agronomy, 11(4), 667. https://doi.org/10.3390/agronomy11040667
Aslan, B., & Aslan, F. Y. (2025). Bitkisel tarımda kullanılan ürün izleme teknolojilerinin incelenmesi. Manas Journal of Agriculture Veterinary and Life Sciences, 15(1), 132-145. https://doi.org/10.53518/mjavl.1599535
Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012). IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology. Decision support systems, 54(1), 510-520. https://doi.org/10.1016/j.dss.2012.07.002
Bala, B. K. (2016). Drying and storage of cereal grains. John Wiley & Sons. ISBN: 9781119124238
Banhazi, T. M., Lehr, H., Black, J. L., Crabtree, H., Schofield, P., Tscharke, M., & Berckmans, D. (2012). Precision livestock farming: an international review of scientific and commercial aspects. International Journal of Agricultural and Biological Engineering, 5(3), 1-9. https://doi.org/10.3965/j.ijabe.20120503.001
Birner, R., Daum, T., & Pray, C. (2021). Who drives the digital revolution in agriculture? A review of supply‐side trends, players and challenges. Applied economic perspectives and policy, 43(4), 1260-1285. https://doi.org/10.1002/aepp.13145
Caja, G., Castro-Costa, A., & Knight, C. H. (2016). Engineering to support wellbeing of dairy animals. Journal of Dairy Research, 83(2), 136-147. https://doi.org/10.1017/S0022029916000261
Dayıoğlu, M. A., & Turker, U. (2021). Digital transformation for sustainable future-agriculture 4.0: A review. Journal of Agricultural Sciences, 27(4), 373-399. https://doi.org/10.15832/ankutbd.986431
Deichmann, U., Goyal, A., & Mishra, D. (2016). Will digital technologies transform agriculture in developing countries? Agricultural Economics, 47(S1), 21-33. https://doi.org/10.1111/agec.12300
Devi, A., Therese, M. J., Dharanyadevi, P., & Pravinkumar, K. (2021). IoT based food grain wastage monitoring and controlling system for warehouse. In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-5). IEEE. https://doi.org/10.1109/ICSCAN53069.2021.9526400
Eastwood, C., Klerkx, L., Ayre, M., & Dela Rue, B. (2019). Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation. Journal of agricultural and environmental ethics, 32(5), 741-768. https://doi.org/10.1007/s10806-017-9704-5
Farooq, M. U., Waseem, M., Mazhar, S., Khairi, A., & Kamal, T. (2015). A review on internet of things (IoT). International journal of computer applications, 113(1). http://dx.doi.org/10.5120/19787-1571
Farooq, M. S., Riaz, S., Helou, M. A., Khan, F. S., Abid, A., & Alvi, A. (2022). Internet of things in greenhouse agriculture: a survey on enabling technologies, applications, and protocols. IEEE access, 10, 53374-53397. https://doi.org/10.1109/ACCESS.2022.3166634
Fróna, D., Szenderák, J., & Harangi-Rákos, M. (2019). The challenge of feeding the world. Sustainability, 11(20), 5816. https://doi.org/10.3390/su11205816
Fuentes, S., Viejo, C. G., Tongson, E., & Dunshea, F. R. (2022). The livestock farming digital transformation: implementation of new and emerging technologies using artificial intelligence. Animal health research reviews, 23(1), 59-71. https://doi.org/10.1017/S1466252321000177
Fukatsu, T., & Nanseki, T. (2009). Monitoring system for farming operations with wearable devices utilized sensor networks. Sensors, 9(8), 6171-6184. https://doi.org/10.3390/s90806171
Halachmi, I., Guarino, M., Bewley, J., & Pastell, M. (2019). Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annual review of animal biosciences, 7(1), 403-425. https://doi.org/10.1146/annurev-animal-020518-114851
Hassan, S. I., Alam, M. M., Illahi, U., Al Ghamdi, M. A., Almotiri, S. H., & Su’ud, M. M. (2021). A systematic review on monitoring and advanced control strategies in smart agriculture. Ieee Access, 9, 32517-32548. https://doi.org/10.1109/ACCESS.2021.3057865
Heikkilä, A. M., Vanninen, L., & Manninen, E. (2010). Economics of small-scale dairy farms having robotic milking. https://agris.fao.org/search/en/providers/125346/records/6748a6437625988a371f089d
Helwatkar, A., Riordan, D., & Walsh, J. (2014). Sensor technology for animal health monitoring. International Journal on Smart Sensing and Intelligent Systems, 7(5), 1-6.
Himu, H. A., & Raihan, A. (2024). An overview of precision livestock farming (PLF) technologies for digitalizing animal husbandry toward sustainability. Global Sustainability Research, 3(3), 1-14. https://doi.org/10.56556/gssr.v3i3.954
Hogenboom, J. A., Pellegrino, L., Sandrucci, A., Rosi, V., & D'Incecco, P. (2019). Invited review: Hygienic quality, composition, and technological performance of raw milk obtained by robotic milking of cows. Journal of dairy science, 102(9), 7640-7654. https://doi.org/10.3168/jds.2018-16013
Jacobs, J. A., & Siegford, J. M. (2012). Invited review: The impact of automatic milking systems on dairy cow management, behavior, health, and welfare. Journal of dairy science, 95(5), 2227-2247. https://doi.org/10.3168/jds.2011-4943
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150-164. https://doi.org/10.1016/j.ijin.2022.09.004
Kaloxylos, A., Eigenmann, R., Teye, F., Politopoulou, Z., Wolfert, S., Shrank, C., ... & Kormentzas, G. (2012). Farm management systems and the Future Internet era. Computers and Electronics in Agriculture, 89, 130-144. https://doi.org/10.1016/j.compag.2012.09.002
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and electronics in agriculture, 143, 23-37. https://doi.org/10.1016/j.compag.2017.09.037
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016
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/10.1016/j.tifs.2019.07.034
Knight, C. H. (2020). Sensor techniques in ruminants: more than fitness trackers. Animal, 14 (S1), 187-195. https://doi.org/10.1017/S1751731119003276
Klerkx, L., Jakku, E., & Labarthe, P. A. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS-Wageningen journal of life sciences, 90,100315. https://doi.org/10.1016/j.njas.2019.100315
Le Mouël, C., & Forslund, A. (2017). How can we feed the world in 2050? A review of the responses from global scenario studies. European Review of Agricultural Economics, 44(4), 541-591. https://doi.org/10.1093/erae/jbx006
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
Liu, B., Tao, W., Feng, D., Wang, Y., Heizatuola, N., Ahemetbai, T., & Wu, W. (2022). Revealing genetic diversity and population structure of endangered Altay white-headed cattle population using 100 k SNP markers. Animals, 12(22), 3214. https://doi.org/10.3390/ani12223214
Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and precision livestock farming (PLF): an up to date overview across animal productions. Sensors, 22(12), 4319. https://doi.org/10.3390/s22124319
Nasirahmadi, A., Edwards, S. A., & Sturm, B. (2017). Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science, 202, 25-38. https://doi.org/10.1016/j.livsci.2017.05.014
Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367. https://doi.org/10.1016/j.sbsr.2020.100367
Nie, J., Wang, Y., Li, Y., & Chao, X. (2022). Sustainable computing in smart agriculture: survey and challenges. Turkish Journal of Agriculture and Forestry, 46(4), 550-566. https://doi.org/10.55730/1300-011X.3025
Nogalski, Z., Czerpak, K., & Pogorzelska, P. (2011). Effect of automatic and conventional milking on somatic cell count and lactation traits in primiparous cows. Ann. Anim. Sci, 11(3), 433-441. https://www.researchgate.net/publication/270812970_Effect_of_automatic_and_conventional_milking_on_somatic_cell_count_and_lactation_traits_in_primiparous_cows
Oduniyi, O. S., Antwi, M. A., & Mukwevho, A. N. (2021). Assessing emerging beef farmers participation in high-value market and its impact on cattle sales in South Africa. International Journal of Agricultural Research, Innovation and Technology (IJARIT), 11(2), 27-36. https://doi.org/10.22004/ag.econ.317005
O’Sullivan, J. N. (2023). Demographic delusions: world population growth ıs exceeding most projections and jeopardising scenarios for sustainable futures. World, 4(3), 545-568. https://doi.org/10.3390/world4030034
Phillips, T. W., & Throne, J. E. (2010). Biorational approaches to managing stored-product insects. Annual Review of Entomology, 55(1), 375-397. https://doi.org/10.1146/annurev.ento.54.110807.090451
Rehman, A., Saba, T., Kashif, M., Fati, S. M., Bahaj, S. A., & Chaudhry, H. (2022). A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy, 12(1), 127. https://doi.org/10.3390/agronomy12010127
Ra, S., Ahmed, M., & Teng, P. S. (2019). Creating high-tech ‘agropreneurs’ through education and skills development. International Journal of Training Research, 17(sup1), 41-53. https://doi.org/10.1080/14480220.2019.1629736
Rojas-Downing, M. M., Nejadhashemi, A. P., Harrigan, T., & Woznicki, S. A. (2017). Climate change and livestock: Impacts, adaptation, and mitigation. Climate risk management, 16, 145-163. https://doi.org/10.1016/j.crm.2017.02.001
Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2, 87. https://doi.org/10.3389/fsufs.2018.00087
Rutten, C. J., Velthuis, A. G. J., Steeneveld, W., & Hogeveen, H. (2013). Invited review: Sensors to support health management on dairy farms. Journal of dairy science, 96(4), 1928-1952. https://doi.org/10.3168/jds.2012-6107
Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2), 207. https://doi.org/10.3390/agronomy10020207
Sevli, O. (2023). TARIM 4.0 ölçeğinde bir dijital tarım uygulaması: Çiftlik izleme ve yönetim sistemi. Uluslararası Sürdürülebilir Mühendislik ve Teknoloji Dergisi, 7(2), 105-116. https://dergipark.org.tr/en/pub/usmtd/issue/80956/1354920
Shen, W., Cheng, F., Zhang, Y., Wei, X., Fu, Q., & Zhang, Y. (2020). Automatic recognition of ingestive-related behaviors of dairy cows based on triaxial acceleration. Information Processing in Agriculture, 7(3), 427-443. https://doi.org/10.1016/j.inpa.2019.10.004
Simo, A., Dzitac, S., Badea, G. E., & Meianu, D. (2022). Smart agriculture: IoT-based greenhouse monitoring system. International Journal of Computers Communications & Control, 17(6). https://doi.org/10.15837/ijccc.2022.6.5039
Sinha, B. B., & Dhanalakshmi, R. (2022). Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems, 126, 169-184. https://doi.org/10.1016/j.future.2021.08.006
Skendžić, S., Zovko, M., Živković, I. P., Lešić, V., & Lemić, D. (2021). The impact of climate change on agricultural insect pests. Insects, 12(5), 440. https://doi.org/10.3390/insects12050440
Şahin Suci, E. (2023). Konya bölgesindeki hububat silolarının yapısal ve iklimsel analizi. (Publication No. 822264) (Doktora Tezi, Selçuk Üniversitesi)
Tian, F. (2016). An agri-food supply chain traceability system for China based on RFID & blockchain technology. 13th international conference on service systems and service management (ICSSSM) (pp. 1-6). IEEE. https://doi.org/10.1109/ICSSSM.2016.7538424
Tian, F., Wang, J., Xiong, B., Jiang, L., Song, Z., & Li, F. (2021). Real-time behavioral recognition in dairy cows based on geomagnetism and acceleration information. IEEe Access, 9, 109497-109509.
https://doi.org/10.1109/ACCESS.2021.3099212
Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems engineering, 164, 31-48. https://doi.org/10.1016/j.biosystemseng.2017.09.007
Volosciuc, C., Bogdan, R., Blajovan, B., Stângaciu, C., & Marcu, M. (2024). GreenLab, an IoT-Based small-scale smart greenhouse. Future Internet, 16(6), 195. https://doi.org/10.3390/fi16060195
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/10.1016/j.agsy.2017.01.023
Yılmaz, A., & Doğan, H. (2016). Sera İçi Hava Şartlarının Otomasyon Sistemi İle Üretim Kalitesinin Artırılması ile İlgili Bir Çalışma. Batman Üniversitesi Yaşam Bilimleri Dergisi, 6(2/2), 145-159. https://dergipa
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
