Soil and Sustainable Development

Soil and Sustainable Development

The Transformation of Five Generations of Agriculture: From Manual Labor to Artificial Intelligence and Automation

Document Type : Review Article

Author
Arezoo Hassanvand
Ph.D. Candidate, Department of Agricultural Management and Development, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran. Karaj, IRAN.
10.22034/ssd.2025.532202.1013
Abstract
hroughout history, agriculture has undergone remarkable transformations, with each generation marked by technological advancements and fundamental changes in production methods. This article examines the evolution of five generations of agriculture, from the first generation, which relied on manual labor and animal power, to the fifth generation, which integrates advanced technologies such as artificial intelligence (AI), robotics, the Internet of Things (IoT), and automation, elevating agriculture to an entirely new level. The first generation of agriculture was characterized by traditional practices and simple tools, while the second generation brought about a revolution in productivity through mechanization and the use of machinery. The third generation, marked by the advent of biotechnology and genetic engineering, ushered in the Green Revolution. The fourth generation, leveraging digital and data-driven technologies, enabled precision and smart agriculture. Now, the fifth generation of agriculture focuses on human-machine collaboration, utilizing AI and automated systems to create sustainable, efficient, and environmentally friendly agricultural practices. In summary, the five-generation evolution of agriculture demonstrates this industry's progressive shift toward greater intelligence and reduced reliance on human labor. However, challenges such as infrastructure availability, cost-effectiveness of technology for smallholder farmers, and ethical considerations in artificial intelligence applications must be carefully addressed. To overcome these obstacles, necessary coordination through joint investments in research and development, establishment of international standards, and implementation of supportive policies is essential..
Keywords
Agricultural Evolution
Artificial Intelligence (AI)
Automation
Fifth-Generation Agriculture (5.0)
Subjects
Ameen, A. & Raza, S. (2017). Green revolution: a review. International Journal of Advances in Scientific Research, 3(12), 129-137.
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.
Beluhova-Uzunova, R., & Dunchev, D. (2022). Agriculture 4.0-concepts, technologies and prospects. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development. Vol. 22, Issue 2, 2022. ISSN 2284-7995, E-ISSN 2285-3952.
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. doi:10.1016/j.agwat.2021.107324.
Cordeiro, M., Markert, C., Araújo, S.S., Campos, N.G.S., Gondim, R.S., da Silva, T.L.C., & da Rocha, A.R. (2022). Towards Smart Farming: Fog-enabled intelligent irrigation system using deep neural networks. Future Generation Computer Systems, 129, 115–124. doi:10.1016/j.future.2021.11.013.
FAO. (2017).The future of food and agriculture–Trends and challenges. Annual Report 296, 2017.
Gyamfi, E.K., ElSayed, Z., Kropczynski, J., Yakubu, M.A., & Elsayed, N. (2024). Agricultural 4.0 leveraging on technological solutions: Study for smart farming sector. In 2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI) (pp. 1-9). IEEE.
Huh, J. H., & Kim, K. Y. (2018). Time-based trend of carbon emissions in the composting process of swine manure in the context of agriculture 4.0. Processes, 6(9), 168.
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. doi:10.1016/j.ijin.2022.09.004.
Kodan, R., Parmar, P., & Pathania, S. (2020). Internet of things for food sector: Status quo and projected potential. Food Reviews International, 36(6), 584-600.
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, vol. 24, March 2020.
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, vol. 90-91, December 2019.
Naikwade, R.R., Patle, B.K., Joshi, V.S., Pagar, N.D., & Hirwe, S.B. (2021). Agriculture 5.0: Future of Smart Farming. National Conference on Innovative Global Technology Trends in Art, Design, Technology, Management, Vedic Science, Education and Architecture, Film & Media, 1-6.
Sharifulden, N.S.A.N. (2024).What Does it Take to Transform Malaysia’s Agriculture Sector into a High-Tech Industry? Kuala Lumpur: Khazanah Research Institute. License: Creative Commons Attribution CC BY 3.0.
Nyangaresi, V.O., El-Omari, N.K.T., & Nyakina, J.N. (2022). Efficient Feature Selection and ML Algorithm for Accurate Diagnostics. Journal of Computer Science Research, 4(1), 10–19. doi:10.30564/jcsr.v4i1.3852.
Otieno, M. (2023). An extensive survey of smart agriculture technologies: Current security posture. World Journal of Advanced Research and Reviews, 18(3), 1207–1231. doi:10.30574/wjarr.2023.18.3.1241.
Pivoto, D., Barham, B., Waquil, P. D., Foguesatto, C. R., Dalla Corte, V. F., Zhang, D., & Talamini, E. (2019). Factors influencing the adoption of smart farming by Brazilian grain farmers. International Food and Agribusiness Management Review, 22(4), 571-588.
Polymeni, S., Plastras, S., Skoutas, D.N., Kormentzas, G., & Skianis, C. (2023). The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review. Electronics, 12(12), 2651. doi:10.3390/electronics12122651.
Quiroz, I.A., & Alférez, G.H. (2020). Image recognition of Legacy blueberries in a Chilean smart farm through deep learning. Computers and Electronics in Agriculture, 168, 105044.
Race, D., Gentle, P., & Mathew, S. (2023). Living on the margins: Climate change impacts and adaptation by remote communities living in the Pacific Islands, the Himalaya and desert Australia. Climate Risk Management, 40. doi:10.1016/j.crm.2023.100503.
Rapela, M.A. (2019). A comprehensive solution for agriculture 4.0. In: Fostering Innovation for Agriculture 4.0. Springer, pp. 53–69. http://dx.doi.org/10.1007/978-3-030-32493-3.
Razak, S.F.A., Yogarayan, S., Sayeed, M.S., & Derafi, M.I.F.M. (2024). Agriculture 5.0 and explainable ai for smart agriculture: A scoping review. Emerging Science Journal, 8(2), 744-760.
Rodríguez, J.P., Montoya-Munoz, A.I., Rodriguez-Pabon, C., Hoyos, J., & Corrales, J.C. (2021). IoT-Agro: A smart farming system to Colombian coffee farms. Computers and Electronics in Agriculture, 190. doi:10.1016/j.compag.2021.106442.
Sadiku, M.N.O, Chukwu, U.C, Majebi, A.A., & Musa, S.M. (2021). Introduction to Agriculture 4.0. Journal of Scientific and Engineering Research, 2021, 8(4):121-128.
Sharma, V., Tripathi, A.K., & Mittal, H. (2022). Technological revolutions in smart farming: Current trends, challenges & future directions. Computers and Electronics in Agriculture, 201, 107217.
Shrivastava, A., Nayak, C.K., Dilip, R., Samal, S.R., Rout, S., & Ashfaque, S.M. (2023). Automatic robotic system design and development for vertical hydroponic farming using IoT and big data analysis. Materials Today: Proceedings, 80, 3546–3553. doi:10.1016/j.matpr.2021.07.294.
Thakur, N., Nigam, M., Mann, N.A., Gupta, S., Hussain, C.M., Shukla, S.K., Shah, A.A., Casini, R., Elansary, H.O., & Khan, S.A. (2023). Host-mediated gene engineering and microbiome-based technology optimization for sustainable agriculture and environment. Functional and Integrative Genomics, 23(1). doi:10.1007/s10142-023-00982-9.
Vanghele, N.A., Petre, A.A., Matache, A., & Stanciu, M.M. (2021). AGRICULTURE 5.0-REVIEW. Annals of the University of Craiova-Agriculture, Montanology, Cadastre Series, 51(2), 576-583.
Wolfert, S., Goense, D., & Sorensen, C.A.G. (2014). A future internet collaboration platform for safe and healthy food from farm to fork. Annual SRII Global Conference, SRII, pp. 266–273.
Zambon, I., Cecchini, M., Egidi, G., Saporito, M.G., & Colantoni, A. (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs. Processes, 7(1). doi:10.3390/pr7010036.
Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256
Soil and Sustainable Development
Volume 1, Issue 2
Autumn 2025
Pages 194-206

  • Receive Date 02 July 2025
  • Revise Date 15 July 2025
  • Accept Date 04 August 2025
  • Publish Date 23 September 2025