Abstract
A revolutionary paradigm change in agriculture is being referred to as “digital” or “precision” farming, which aims to address issues with global food security while advancing environmental sustainability and economic success. Through the amalgamation of technologies resemble Internet of Things (IoT) sensors, drones, artificial intelligence, plus big data, this maximizes the sustainability, efficiency, and productivity of the farming sector by optimizing different aspects of farming operations with traditional agricultural practices. In addition, it uses sensors for remote sensing and data analytics to track crop health, moisture content, and soil conditions in real time. This allows farmers to decide cognizant decisions about pest control, fertilization, and irrigation. Drones with multispectral cameras and remote sensing data analytics sensors may accurately and efficiently provide airborne footage for crop monitoring, disease diagnosis, and yield estimation. This allows for insights into agricultural performance, weather patterns, and other related topics. Furthermore, it makes it easier for sustainable practices to be adopted by lowering input consumption, lessening environmental effect, and improving resource efficiency. Digital farming systems are networked, allowing for easy integration with supply chains and quality assurance, traceability, and transparency from farm to fork. Additionally, farmers can boost their competitiveness in the market by utilizing blockchain technology to guarantee the authenticity and integrity of their produce, earning the trust of customers. The present review critically scrutinizes the sways of big data systems and emerging technologies on agriculture (the “digital revolution”). Additionally, it highlights the contributions of big data sways, the Internet of Things (IoT), besides cloud computing focusing on a number of important issues and challenges.
Graphical Abstract
Similar content being viewed by others
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Abioye, E. A., Hensel, O., Esau, T. J., Elijah, O., Abidin, M. S. Z., Ayobami, A. S., Yerima, O., & Nasirahmadi, A. (2022). Precision irrigation management using machine learning and digital farming solutions. AgriEngineering, 4(1), 70–103.
Agarwal, V., Malhotra, S., & Dagar, V. (2023). Co** with public-private partnership issues: A path forward to sustainable agriculture. Socio-Economic Planning Sciences, 89, 101703.
Ahmadi, S., AlKafaas, S. S., Aziz, S. A., Ammar, E. E., Elsalahaty, M. I., Bedair, H., Oroke, A., Zafer, M. M., Pourebrahimi, S., & Ghosh, S. (2024). Effects of particulate matter on human health. Health and Environmental Effects of Ambient Air Pollution.
Aioub, A. A. A., Ghosh, S., AL-Farga, A., Khan, A. N., Bibi, R., Elwakeel, A. M., Nawaz, A., Sherif, N. T., Elmasry, S. A., & Ammar, E. E. (2024). Back to the origins: Biopesticides as promising alternatives to conventional agrochemicals. Eur. J. Plant Pathol.
Akhter, R., & Sofi, S. A. (2022). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences, 34(8), 5602–5618.
Alvarado, R., Tillaguango, B., Dagar, V., Ahmad, M., Işık, C., Méndez, P., & Toledo, E. (2021). Ecological footprint, economic complexity and natural resources rents in Latin America: Empirical evidence using quantile regressions. Journal of Cleaner Production, 318, 128585.
Alvarado, R., Tillaguango, B., Cuesta, L., Pinzon, S., Alvarado-Lopez, M. R., Işık, C., & Dagar, V. (2022). Biocapacity convergence clubs in Latin America: An analysis of their determining factors using quantile regressions. Environmental Science and Pollution Research, 29(44), 66605–66621.
Ammar, E. E. (2022). Environmental Impact of Biodegradation. In A. S. H. Ali, & G. A. M. Makhlouf (Eds.), Handbook of biodegradable materials (pp. 1–40). Springer. https://doi.org/10.1007/978-3-030-83783-9_27-1.
Ammar, E. E., Aioub, A. A. A., Elesawy, A. E., Karkour, A. M., Mouhamed, M. S., Amer, A. A., & EL-Shershaby, N. A. (2022). Algae as bio-fertilizers: Between current situation and future prospective: The role of Algae as a bio-fertilizer in serving of ecosystem. Saudi J Biol Sci, 29, 3083–3096.
Ammar, E. E., Rady, H. A., Khattab, A. M., Amer, M. H., Mohamed, S. A., Elodamy, N. I., AL-Farga, A., & Aioub, A. A. A. (2023). A comprehensive overview of eco-friendly bio-fertilizers extracted from living organisms. Environmental Science and Pollution Research, 30(53), 113119–113137. https://doi.org/10.1007/s11356-023-30260-x.
Avram, M. G. (2014). Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology, 12, 529–534.
Basri, R., Islam, F., Shorif, S. B., & Uddin, M. S. (2021). Robots and drones in agriculture - a survey. Computer Vision and Machine Learningin Agriculture,9–29.
Benke, K., & Tomkins, B. (2017). Tomkins Future food-production systems: Vertical farming and controlled-environment agriculture. Sustain Sci Pract Policy, 13, 13–26.
Bhardwaj, M., Kumar, P., Kumar, S., Dagar, V., & Kumar, A. (2022). A district-level analysis for measuring the effects of climate change on production of agricultural crops, ie, wheat and paddy: Evidence from India. Environmental Science and Pollution Research, 29(21), 31861–31885.
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.
Bokhari, M. U., Shallal, Q. M., & Tamandani, Y. K. (2016). Cloud computing service models: A comparative study. 2016 3rd International Conference on Computing for Sustainable Global Development(INDIACom),890–895.
Bronson, K., & Knezevic, I. (2016). Big Data in food and agriculture. Big Data & Society, 3(1), 2053951716648174.
Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324.
Cambra Baseca, C., Sendra, S., Lloret, J., & Tomas, J. (2019). A smart decision system for digital farming. Agronomy, 9(5), 216.
Carolan, M. (2017). Publicising food: Big data, precision agriculture, and co-experimental techniques of addition. Sociologia Ruralis, 57(2), 135–154.
Carolan, M. (2018). Smart’farming techniques as political ontology: Access, sovereignty and the performance of neoliberal and not-s-neoliberal worlds. Sociologia Ruralis, 58(4), 745–764.
Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., & Zhu, Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104(11), 2207–2219.
Clapp, J., & Ruder, S. L. (2020). Precision technologies for agriculture: Digital farming, gene-edited crops, and the politics of sustainability. Global Environmental Politics, 20(3), 49–69.
Cravero, A., & Sepúlveda, S. (2021). Use and adaptations of machine learning in big data—applications in real cases in agriculture. Electronics, 10(5), 552.
Dagar, V., Khan, M. K., Alvarado, R., Usman, M., Zakari, A., Rehman, A., & Tillaguango, B. (2021). Variations in technical efficiency of farmers with distinct land size across agro-climatic zones: Evidence from India. Journal of Cleaner Production, 315, 128109.
Dayioğlu, M. A., & Turker, U. (2021). Digital transformation for sustainable future-agriculture 4.0: A review. Journal of Agricultural Sciences, 27(4), 373–399.
De Mauro, A., Greco, M., & Grimaldi, M. (2016). A formal definition of Big Data based on its essential features. Library Review, 65(3), 122–135.
Dhal, S., Wyatt, B. M., Mahanta, S., Bhattarai, N., Sharma, S., Rout, T., Saud, P., & Acharya, B. S. (2023). Internet of things (IoT) in digital agriculture: An overview. Agronomy Journal. https://doi.org/10.1002/agj2.21385.
Dozono, K., Amalathas, S., & Saravanan, R. (2022). The impact of cloud computing and artificial intelligence in digital agriculture. Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 1, 557–569.
El-Shinnawy, N. A., Heikal, S., & Fahmy, Y. (1983). Saccharification of cotton bolls by concentrated sulphuric acid. Research and Industry, 28(2), 123–126.
Erickson, B., & Widmar, D. A. (2015). Precision Agricultural Services Dealership Survey Results. West Lafayette, Indiana: Purdue University. Available online: https://agribusiness.purdue.edu/wp-content/uploads/2019/08/2015-crop-life-purdue-precisiondealer-survey.pdf (accessed on 27 January 2021).
European Commission. European Union Funds Digital Research and Innovation for Agriculture to Tackle Societal Challenges (2018). Available online: https://ec.europa.eu/info/news/european-union-funds-digital-research-and-innovation-agriculturetackle-societal-challenges_en (accessed on 27 January 2021).
Fagier, M. A. (2021). Plant-mediated biosynthesis and photocatalysis activities of zinc oxide nanoparticles: a prospect towards dyes mineralization. Journal of Nanotechnology, 2021, 1–15.
Fahmy, Y. (1982). Pyrolysis of agricultural residues. I. prospects of lignocellulose pyrolysis for producing chemicals and energy sources. Cellulose Chemistry and Technology, 16, 347–355.
Fahmy, T. Y. A. (2017). Molasses as a new additive in papermaking: For Bagasse and Kaolin Filled Bagasse pulps. Professional Papermaking, 2017-June(1), 26–29.
Fahmy, Y., Fadl, M. H., & El-Shinnawy, N. A. (1975). Saccharification of cotton stalks. Research and Industry, 20(1), 7–10.
Fahmy, Y., Mobarak, F., & Schweers, W. (1982). Pyrolysis of agricultural residues. II. Yield and chemical composition of tars and oils produced from cotton stalks, and assessment of lignin structure. Cellulose Chemistry and Technology, 16(January 1982), 453–459.
Fahmy, Y., Fahmy, T. Y. A., Mobarak, F., El-Sakhawy, M., & Fadl, M. H. (2017). Agricultural residues (wastes) for manufacture of Paper, Board, and miscellaneous products: Background overview and future prospects. International Journal of ChemTech Research, 10(2), 424–448. https://doi.org/10.5281/zenodo.546735.
Fahmy, T. Y. A., Fahmy, Y., Mobarak, F., El-Sakhawy, M., & Abou-Zeid, R. E. (2020). Biomass pyrolysis: Past, present, and future. Environment Development and Sustainability, 22(1), 17–32. https://doi.org/10.1007/s10668-018-0200-5.
FAO. (2020). Status of Digital Agriculture in 18 countries of Europe and Central Asia. International Telecommunication Union and Food and Agriculture Organization of the United Nations.
Farooq, A., Laato, S., & Najmul Islam, A. (2020a). Impact of online information on self-isolation intention during the COVID-19 Pandemic: Cross-sectional study. Journal of Medical Internet Research, 22(5), 1–15. https://doi.org/10.2196/19128.
Farooq, M. S., Riaz, S., Abid, A., Umer, T., Zikria, Y., & Bin (2020b). Role of IoT technology in agriculture: A systematic literature review. Electronics, 9(2), 319.
Fleming, A., Jakku, E., Lim-Camacho, L., Taylor, B., & Thorburn, P. (2018). Is big data for big farming or for everyone? Perceptions in the Australian grains industry. Agronomy for Sustainable Development, 38, 1–10.
Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., Liakos, B., Canavari, M., Wiebensohn, J., & Tisserye, B. al (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40–50.
Fountas, S., Espejo-Garcia, B., Kasimati, A., Mylonas, N., & Darra, N. (2020). The future of digital agriculture: Technologies and opportunities. IT Professional, 22(1), 24–28.
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.
García, L., Parra, L., Jimenez, J. M., Lloret, J., & Lorenz, P. (2020). IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors (Basel, Switzerland), 20(4), 1042.
Goedde, L., Katz, J., Ménard, A., & Revellat, J. (2020). One of the Oldest Industries Must Embrace a Digital, Connectivity-Fueled Transformation in Order to Overcome Increasing Demand and Several Disruptive Forces. Available online: https://www.mckinsey.com/industries/agriculture/our-insights/agricultures-connected-future-how-technology-can-yield-new-growth#(accessed on 27 January 2021).
Gondchawar, N., & Kawitkar, R. S. (2016). IoT based smart agriculture. International Journal of Advanced Research in Computer and Communication Engineering, 5(6), 838–842.
Goraya, M., S., & Kaur, H. (2015). Cloud computing in agriculture. HCTL Open International Journal of Technology Innovations and Research, 16, 1–5.
Guru, S., Verma, S., Baheti, P., & Dagar, V. (2023). Assessing the feasibility of hyperlocal delivery model as an effective distribution channel. Management Decision, 61(6), 1634–1655.
Hasan, M. (2020). Real-time and low-cost IoT based farming using raspberry pi. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), 197–204.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing: Review and open research issues. Information Systems, 47, 98–115.
Humayun, M. (2020). Role of emerging IoT big data and cloud computing for real time application. International Journal of Advanced Computer Science and Applications, 11(4), 494–506.
Huo, D., Malik, A. W., Ravana, S. D., Rahman, A. U., & Ahmedy, I. (2024). Map** smart farming: Addressing agricultural challenges in data-driven era. Renewable and Sustainable Energy Reviews, 189(PA), 113858. https://doi.org/10.1016/j.rser.2023.113858.
Ingram, J., & Maye, D. (2020). What are the implications of Digitalisation for Agricultural Knowledge? Frontiers in Sustainable Food Systems, 4, 66.
Issa, A. A., Majed, S., Ameer, S. A., & Al-jawahry, H. M. (2024). Farming in the Digital Age: Smart Agriculture with AI and IoT. 00081.
Jaiswal, S., & Rawat, G. (2021). IoT-Enabled Smart Farming: Challenges and Opportunities. Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT, 123–139.
Jeanneaux, P. (2017). Digital agriculture: The end of the farmer’s decision-making? Changes in Sustainable Organization and Food Sector Management, 11.
Kamilaris, A., Gao, F., Prenafeta-Boldu, F. X., & Ali, M. I. (2016). Agri-IoT: A semantic framework for internet of things-enabled smart farming applications. 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), 442, 447.
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. (2017). X. A review on the practice of big data analysis in agriculture. In Computers and Electronics in Agriculture (Vol. 143, pp. 23–37). https://doi.org/10.1016/j.compag.2017.09.037.
Karkee, M., & Zhang, Q. (2021). Fundamentals of agricultural and field robotics. Springer.
Kashina, E., Yanovskaya, G., Fedotkina, E., Tesalovsky, A., Vetrova, E., Shaimerdenova, A., & Aitkazina, M. (2022). Impact of Digital Farming on Sustainable Development and Planning in Agriculture and increasing the competitiveness of the Agricultural Business. International Journal of Sustainable Development and Planning, 17(8), 2413–2420.
Kassim, M. R. M. (2020). Iot applications in smart agriculture: Issues and challenges. 2020 IEEE Conference on Open Systems (ICOS), 19–24.
Kirkaya, A. (2020). Smart farming-precision agriculture technologies and practices. Journal of Scientific Perspectives, 4(2), 123–136.
Lasdun, V. (2012). Peer learning in a Digital Farmer-to-Farmer Network: Effects on Technology Adoption and Self-Efficacy beliefs Violet. European University Institute, 2, 2–5.
Lehmann, J., Bossio, D. A., Kögel-Knabner, I., & Rillig, M. C. (2020). The concept and future prospects of soil health. Nature Reviews Earth & Environment, 1(10), 544–553. https://doi.org/10.1038/s43017-020-0080-8.
Li, D. (2020). China Rural Informatization Development Report (2019); China Machine Press: Bei**g, China. (In Chinese).
Madushanki, A. A. R., Halgamuge, M. N., Wirasagoda, W. A. H. S., & Syed, A. (2019). Adoption of the internet of things (IoT) in agriculture and smart farming towards urban greening: A review. International Journal of Advanced Computer Science and Applications, 10(4), 11–28.
Mobarak, F. (1983). Rapid continuous pyrolysis of cotton stalks for charcoal production. Holzforschung, 37(5), 251–254.
Mobarak, F., Fahmy, Y., & Augustin, H. (1982a). Binderless lignocellulose composite from bagasse and mechanism of self-bonding. Holzforschung, 36(3), 131–136. https://doi.org/10.1515/hfsg.1982.36.3.131.
Mobarak, F., Fahmy, Y., & Schweers, W. (1982b). Production of phenols and charcoal from bagasse by a rapid continuous pyrolysis process. Wood Science and Technology, 16, 59–66.
Mohamed, E. S., Belal, A. A., Abd-Elmabod, S. K., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 971–981.
Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Big data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96(4), 1540–1550.
Naresh, M., & Munaswamy, P. (2019). Smart agriculture system using IoT technology. International Journal of Recent Technology and Engineering (Vol, 7(5), 98–102.
Newell, P., & Taylor, O. (2018). Contested landscapes: The global political economy of climate-smart agriculture. The Journal of Peasant Studies, 45(1), 108–129.
Nidhi (2020). Big Data for Smart Agriculture. Smart Village Technology: Concepts and Developments, 181–189.
Oussous, A., Benjelloun, F., Lahcen, Z., A, A., & Belfkih, S. (2018). Big Data technologies: A survey. Journal of King Saud University - Computer and Information Sciences, 30(4), 431–448. https://doi.org/10.1016/j.jksuci.2017.06.001.
Paraforos, D. S., & Griepentrog, H. W. (2021). Digital farming and field robotics: Internet of things, cloud computing, and big data. Fundamentals of Agricultural and Field Robotics, 365–385.
Prasad, M. R., Naik, R. L., & Bapuji, V. (2013). Cloud computing: Research issues and implications. International Journal of Cloud Computing and Services Science, 2(2), 134–140.
Punjani, K. K., Mahadevan, K., Gunasekaran, A., Kumar, V. V. R., & Joshi, S. (2023). Cloud computing in agriculture: A bibliometric and network visualization analysis. Quality & Quantity, 57(4), 3849–3883.
Rose, D. C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible Innovation in an era of Smart Farming. Frontiers in Sustainable Food Systems, 2., Article 87. https://doi.org/10.3389/fsufs.2018.00087.
Rose, D. C., Wheeler, R., Winter, M., Lobley, M., & Chivers, C. A. (2020). Agriculture 4.0: Making it work for people, production, and the planet. Land Use Policy, 100, 104933.
Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., Reed, M., & Fraser, E. D. G. (2019). The politics of digital agricultural technologies: A preliminary review. Sociol Rural, 59, 203–229.
Rust, N. A., Stankovics, P., Jarvis, R. M., Morris-Trainor, Z., de Vries, J. R., Ingram, J., Mills, J., Glikman, J. A., Parkinson, J., & Toth, Z. (2022). Have farmers had enough of experts? Environmental Management, 1–14.
Saini, M. K., & Saini, R. K. (2022). Smart agriculture using internet of things: an empirical study. In Recent Innovations in Computing: Proceedings of ICRIC 2021, Volume 2 (pp. 163–175). Springer.
Sarker, M. N. I., Islam, M. S., Ali, M. A., Islam, M. S., Salam, M. A., & Mahmud, S. M. H. (2019). Promoting digital agriculture through big data for sustainable farm management. International Journal of Innovation and Applied Studies, 25(4), 1235–1240.
Sarker, M. N. I., Islam, M. S., Murmu, H., & Rozario, E. (2020). Role of big data on digital farming. Int J Sci Technol Res, 9(4), 1222–1225.
Shamshiri, R. R., Weltzien, C., Hameed, I. A., Yule, I. J., Grift, T. E., Balasundram, S. K., Pitonakova, L., Ahmad, D., & Chowdhary, G. (2018). Research and development in agricultural robotics: A perspective of digital farming. International Journal of Agricultural and Biological Engineering, 11(4), 1–14.
Shang, L., Heckelei, T., Gerullis, M. K., Börner, J., & Rasch, S. (2021). Adoption and diffusion of digital farming technologies-integrating farm-level evidence and system interaction. Agricultural Systems, 190, 103074.
Singh, T., & Asim, M. (2021). Weather monitoring system using IoT. Innovations in Cyber Physical Systems: Select Proceedings of ICICPS 2020, 247–253.
Soma, T., & Nuckchady, B. (2021). Communicating the benefits and risks of digital agriculture technologies: Perspectives on the future of digital agricultural education and training. Frontiers in Communication, 6, 259.
Sonka, S. T. (2020). Digital Technologies, Big Data, and Agricultural Innovation. The Innovation Revolution in Agriculture, 207.
Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2018). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78, 964–975.
Sundmaeker, H., Verdouw, C., Wolfert, S., & Freire, L. P. (2022). Internet of food and farm 2020. Digitising the industry internet of things connecting the Physical, Digital and VirtualWorlds (pp. 129–151). River.
Symeonaki, E., Arvanitis, K. G., & Piromalis, D. D. (2017). Review on the Trends and Challenges of Cloud Computing Technology in Climate-Smart Agriculture. HAICTA, 66–78.
Tsai, C., Lai, W., Chao, C. F., H, C., & Vasilakos, A., V (2015). Big data analytics: A survey. Journal of Big Data, 2(1), 1–32. https://doi.org/10.1186/s40537-015-0030-3.
Tsan, M., Totapally, S., Hailu, M., & Addom, B. K. (2019). The Digitalisation of African Agriculture Report 2018–2019. Wageningen, The Netherlands, CTA. Available online: https://www.cta.int/en/digitalisation/article/measuring-the-outcomes-of-thedigitalisation-of-african-agriculture-report-2018-2019-sid016f9505d-4461-4fe4-8dc3-df4b7bc1726d (accessed on 27 January 2021).
Tsouros, D., Triantafyllou, C., Bibi, A., S., & Sarigannidis, P. (2019). G. Data acquisition and analysis methods in UAV- based applications for precision agriculture. Proceedings – 15th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2019, 377–384. https://doi.org/10.1109/DCOSS.2019.00080.
Turner, R. J., Geraghty, N. J., Williams, J. G., Ly, D., Brungs, D., Carolan, M. G., Guy, T. V., Watson, D., de Leon, J. F., & Sluyter, R. (2020). Comparison of peripheral blood mononuclear cell isolation techniques and the impact of cryopreservation on human lymphocytes expressing CD39 and CD73. Purinergic Signalling, 16(3), 389–401.
Uddin, M. A., Ayaz, M., Mansour, A., Aggoune, H. M., Sharif, Z., & Razzak, I. (2021). Cloud-connected flying edge computing for smart agriculture. Peer-to-Peer Networking and Applications, 14(6), 3405–3415.
Udemba, E. N., Dagar, V., Peng, X., & Dagher, L. (2023). Attaining environmental sustainability amidst the interacting forces of natural resource rent and foreign direct investment: Is Norway any different? OPEC Energy Review.
van der Burg, S., Wiseman, L., & Krkeljas, J. (2021). Trust in farm data sharing: Reflections on the EU code of conduct for agricultural data sharing. Ethics and Information Technology, 23(3), 185–198.
** Countries: A Case Study from China. Land, 10, 245.
Yamada, H., Shimamoto, D., & Wakano, A. (2015). Importance of informal training for the spread of agricultural technologies: Farmers as in-residence extension workers and their motivation for sustainable development. Sustainable Development, 23(2), 124–134.
Young, A. A. (1928). Increasing returns and economic progress. The Economic Journal, 38, 527–542.
Acknowledgements
Not applicable.
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ammar, E.E., Aziz, S.A., Zou, X. et al. An in-depth review on the concept of digital farming. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05161-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10668-024-05161-9