Article

Received 05.04.2023

Revised 21.06.2023

Accepted 29.08.2023

Retrieved from Volume 27, No. 3, 2023

Pages 9 -17

Wrzecińska, M., Czerniawska-Piątkowska, E., Kowalewska, I., Kowalczyk, A., Mylostyvyi, R., & Stefaniak, W. (2023). Agriculture in the face of new digitization technologies. Ukrainian Black Sea Region Agrarian Science, 27(3), 9-17. https://doi.org/10.56407/bs.agrarian/3.2023.09

Agriculture in the face of new digitization technologies

Marcjanna Wrzecińska Ewa Czerniawska-Piątkowska Inga Kowalewska Alicja Kowalczyk Roman Mylostyvyi Wiktoria Stefaniak

Agriculture plays a vital role in food production, resource utilization, and employment but faces challenges from population growth, climate change, and food shortages. The development of information technology has significantly contributed to the industry's development, and modern technologies such as artificial intelligence, the Internet of Things, computer vision, and machine learning have revolutionized agricultural practices. The purpose of this review is to explore the adoption of digital technologies in agriculture, with a specific focus on their application in livestock breeding. Through the examination of current literature and the utilization of various research methods, this review contributes to the existing knowledge in the field. It is established that the latest information tools allow collecting, analysing data, automating tasks and supporting decision-making, which leads to increased agricultural efficiency, resource management and sustainable development. It has been proven that modern technologies play a crucial role in increasing agricultural production, improving the efficiency of livestock and crop production. These technologies include devices and sensors, data analytics and decision support systems, as well as systems for overall farm productivity assessment. Precision technologies in agriculture, thanks to automation, sensors and machine learning, allow farmers to monitor animal health, optimise feed consumption, detect diseases at early stages and increase overall productivity. IT solutions in agriculture facilitate data processing, visualisation and decision-making, leading to lower costs, greater efficiency and improved food security. The study provides practical insights for farmers and other agricultural stakeholders who can benefit from accurate information, real-time monitoring and automated processes through the integration of modern technologies, ultimately improving agricultural practices and sustainability

sustainable agriculture; digitalization; precision livestock farming; farm management systems; data-driven farming

[1] Abbasi, R., Martinez, P., & Ahmad, R. (2022). The digitization of agricultural industry - a systematic literature review on agriculture 4.0. Smart Agricultural Technology, 2, 100042. doi: 10.1016/j.atech.2022.100042.

[2] Adusumalli, H.P. (2018). Digitization in agriculture: A timely challenge for ecological perspectives authors. Asia Pacific Journal of Energy and Environment, 5(2), 97-102. doi: 10.18034/apjee.v5i2.619.

[3] Antanaitis, R., Juozaitienė, V., Jonike, V., Baumgartner, W., & Paulauskas, A. (2021). Milk lactose as a biomarker of subclinical mastitis in dairy cows. Animals, 11(6), 1736. doi: 10.3390/ani11061736.

[4] Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F., & Pugliese, C. (2022). Review: Precision livestock farming technologies in pasture-based livestock systems. Animal, 16(1), 100429. doi: 10.1016/j.animal.2021.100429.

[5] Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The digitisation of agriculture: A survey of research activities on smart farming. Array, 3-4, 100009. doi: 10.1016/j.array.2019.100009.

[6] Balasundram, S.K., Shamshiri, R.R., Sridhara, S., & Rizan, N. (2023). The role of digital agriculture in mitigating climate change and ensuring food security: An overview. Sustainability, 15(6), 5325. doi: 10.3390/su15065325.

[7] Bao, J., & Xie, Q. (2022). Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production, 331, 129956. doi: 10.1016/j.jclepro.2021.129956.

[8] Chen, H., Liang, Y., Huang, H., Huang, Q., Gu, W., & Liang, H. (2023). Live pig-weight learning and prediction method based on a multilayer RBF network. Agriculture, 13(2), 253. doi: 10.3390/agriculture13020253.

[9] Coşkun, G., Şahin, Ö., Delialioğlu, R.A., Altay, Y., & Aytekin, İ. (2023). Diagnosis of lameness via data mining algorithm by using thermal camera and image processing method in brown swiss cows. Tropical Animal Health and Production, 55(1), 50. doi: 10.1007/s11250-023-03468-9.

[10] De Vries, A., Bliznyuk, N., & Pinedo, P. (2023). Invited review: Examples and opportunities for artificial intelligence (AI) in dairy farms. Applied Animal Science, 39(1), 14-22. doi: 10.15232/aas.2022-02345.

[11] Dhoble, A.S., Ryan, K.T., Lahiri, P., Chen, M., Pang, X., Cardoso, F.C., & Bhalerao, K.D. (2019). Cytometric fingerprinting and machine learning (CFML): A novel label-free, objective method for routine mastitis screening. Computers and Electronics in Agriculture, 162, 505-513. doi: 10.1016/j.compag.2019.04.029.

[12] Džermeikaitė, K., Bačėninaitė, D., & Antanaitis, R. (2023). Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13(5), 780. doi: 10.3390/ani13050780.

[13] Eriksen, E.Ø., Pedersen, K.S., Larsen, I., & Nielsen, J.P. (2022). Evidence-based recommendations for herd health management of porcine post-weaning diarrhea. Animals, 12(14), 1737. doi: 10.3390/ani12141737.

[14] Gabriel, A., & Gandorfer, M. (2023). Adoption of digital technologies in agriculture - an inventory in a European small-scale farming region. Precision Agriculture, 24(1), 68-91. doi: 10.1007/s11119-022-09931-1.

[15] Gamage, A., Gangahagedara, R., Gamage, J., Jayasinghe, N., Kodikara, N., Suraweera, P., & Merah, O. (2023). Role of organic farming for achieving sustainability in agriculture. Farming System, 1(1), 100005. doi: 10.1016/j.farsys.2023.100005.

[16] Hansen, B.G., Bugge, C.T., & Skibrek, P.K. (2020). Automatic milking systems and farmer wellbeing-exploring the effects of automation and digitalization in dairy farming. Journal of Rural Studies, 80, 469-480. doi: 10.1016/j.jrurstud.2020.10.028.

[17] Havelka, Z., Kunes, R., Kononets, Ye., Stokes, J.E., Smutny, L., Olsan, P., Kresan, J., Stehlik, R., Bartos, P., Xiao, M., Kriz, P., Findura, P., & Roztocil, D. (2022). Technology of microclimate regulation in organic and energy-sustainable livestock production. Agriculture, 12(10), 1563. doi: 10.3390/agriculture12101563.

[18] Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., & Müller, H. (2023). AI for life: Trends in artificial intelligence for biotechnology. New Biotechnology, 74, 16-24. doi: 10.1016/j.nbt.2023.02.001.

[19] Javaid, M., Haleem, A., Khan, I.H., & Suman, R. (2023). Understanding the potential applications of artificial intelligence in agriculture sector. Advanced Agrochem, 2(1), 15-30. doi: 10.1016/j.aac.2022.10.001.

[20] Jones, E.J., Bishop, T.F.A., Malone, B.P., Hulme, P.J., Whelan, B.M., & Filippi, P. (2022). Identifying causes of crop yield variability with interpretive machine learning. Computers and Electronics in Agriculture, 192, 106632. doi: 10.1016/j.compag.2021.106632.

[21] Kavlak, A.T., Pastell, M., & Uimari, P. (2023). Disease detection in pigs based on feeding behaviour traits using machine learning. Biosystems Engineering, 226, 132-143. doi: 10.1016/j.biosystemseng.2023.01.004.

[22] Klerkx, L., Jakku, E., & Labarthe, P. (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, 90-91(1), 1-16. doi: 10.1016/j.njas.2019.100315.

[23] Lemmens, L., Schodl, K., Fuerst-Waltl, B., Schwarzenbacher, H., Egger-Danner, C., Linke, K., Suntinger, M., Phelan, M., Mayerhofer, M., Steininger, F., Papst, F., Maurer, L., & Kofler, J. (2023). The combined use of automated milking system and sensor data to improve detection of mild lameness in dairy cattle. Animals, 13(7), 1180. doi: 10.3390/ani13071180.

[24] Mahfuz, Sh., Mun, H.-S., Dilawar, M.A., & Yang, Ch.-J. (2022). Applications of smart technology as a sustainable strategy in modern swine farming. Sustainability, 14(5), 2607. doi: 10.3390/su14052607.

[25] Mendes, J.A.J., Carvalho, N.G.P., Mourarias, M.N., Careta, C.B., Zuin, V.G., & Gerolamo, M.C. (2022). Dimensions of digital transformation in the context of modern agriculture. Sustainable Production and Consumption, 34, 613-637. doi: 10.1016/j.spc.2022.09.027.

[26] Mikula, K., Izydorczyk, G., Skrzypczak, D., Mironiuk, M., Moustakas, K., Witek-Krowiak, A., & Chojnacka, K. (2020). Controlled release micronutrient fertilizers for precision agriculture - a review. Science of The Total Environment, 712, 136365. doi: 10.1016/j.scitotenv.2019.136365.

[27] Myat Noe, S., Thi Zin, T., Tin, P., & Kobayashi, I. (2023). Comparing state-of-the-art deep learning algorithms for the automated detection and tracking of black cattle. Sensors, 23(1), 532. doi: 10.3390/s23010532.

[28] Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367. doi: 10.1016/j.sbsr.2020.100367.

[29] Pawlak, K., & Kołodziejczak, M. (2020). The role of agriculture in ensuring food security in developing countries: Considerations in the context of the problem of sustainable food production. Sustainability, 12(13), 5488. doi: 10.3390/su12135488.

[30] Racewicz, P., Ludwiczak, A., Skrzypczak, E., Składanowska-Baryza, J., Biesiada, H., Nowak, T., Nowaczewski, S., Zaborowicz, M., Stanisz, M., & Ślósarz, P. (2021). Welfare health and productivity in commercial pig herds. Animals, 11(4), 1176. doi: 10.3390/ani11041176.

[31] Robert, M., Thomas, A., & Bergez, J.-E. (2016). Processes of adaptation in farm decision-making models. A review. Agronomy for Sustainable Development, 36(4), 64. doi: 10.1007/s13593-016-0402-x.

[32] Sadjadi, E.N., & Fernández, R. (2023). Challenges and opportunities of agriculture digitalization in Spain. Agronomy, 13(1), 259. doi: 10.3390/agronomy13010259.

[33] Saranya, T., Deisy, C., Sridevi, S., & Anbananthen, K.S.M. (2023). A comparative study of deep learning and Internet of Things for precision agriculture. Engineering Applications of Artificial Intelligence, 122, 106034. doi: 10.1016/j.engappai.2023.106034.

[34] Schori, F., & Münger, A. (2022). Assessment of two wireless reticulo-rumen pH sensors for dairy cows. Agrarforschung Schweiz, 13, 11-16. doi: 10.34776/AFS13-11E.

[35] Shin, J., Mahmud, M.S., Rehman, T.U., Ravichandran, P., Heung, B., & Chang, Y.K. (2022). Trends and prospect of machine vision technology for stresses and diseases detection in precision agriculture. AgriEngineering, 5(1), 20-39. doi: 10.3390/agriengineering5010003.

[36] Singh, A., Jadoun, Y.S., Brar, P.S., & Kour, G. (2022). Smart technologies in livestock farming. In Smart and sustainable food technologies (pp. 25-57). Singapore: Springer. doi: 10.1007/978-981-19-1746-2_2.

[37] Subeesh, A., & Mehta, C.R. (2021). Automation and digitization of agriculture using artificial intelligence and Internet of Things. Artificial Intelligence in Agriculture, 5, 278-291. doi: 10.1016/j.aiia.2021.11.004.

[38] Vrchota, Ja., Pech, M., & Švepešová, I. (2022). Precision agriculture technologies for crop and livestock production in the Czech Republic. Agriculture, 12(8), 1080. doi: 10.3390/agriculture12081080.

[39] Wakchaure, M., Patle, B.K., & Mahindrakar, A.K. (2023). Application of AI techniques and robotics in agriculture: A Review. Artificial Intelligence in the Life Sciences, 3, 100057. doi: 10.1016/j.ailsci.2023.100057.

[40] Wang, Ya., Mücher, S., Wang, W., Guo, L., & Kooistra, L. (2023). A review of three-dimensional computer vision used in precision livestock farming for cattle growth management. Computers and Electronics in Agriculture, 206, 107687. doi: 10.1016/j.compag.2023.107687.

[41] Xie, D., Chen, L., Liu, L., Chen, L., & Wang, H. (2022). Actuators and sensors for application in agricultural robots: A review. Machines, 10(10), 913. doi: 10.3390/machines10100913.

[42] Xu, J., Gu, B., & Tian, G. (2022). Review of agricultural IoT technology. Artificial Intelligence in Agriculture, 6, 10-22. doi: 10.1016/j.aiia.2022.01.001.

[43] Xu, J., Meng, J., & Quackenbush, L.J. (2019). Use of remote sensing to predict the optimal harvest date of corn. Field Crops Research, 236, 1-13. doi: 10.1016/j.fcr.2019.03.003.

[44] Yao, W., & Sun, Zh. (2023). The impact of the digital economy on high-quality development of agriculture: A China case study. Sustainability, 15(7), 5745. doi: 10.3390/su15075745.

[45] Zhu, L. (2022). Digital development of agricultural supply chain finance under rural revitalization strategy. In Proceedings of the 2022 International Conference on urban planning and regional economy（UPRE 2022）(pp. 352-355).doi: 10.2991/aebmr.k.220502.063.

[46] Zorić, N., Marić, R., Đurković-Marić, T., & Vukmirović, G. (2023). The importance of digitalization for the sustainability of the food supply chain. Sustainability, 15(4), 3462. doi: 10.3390/su15043462.