خاک و توسعه پایدار

خاک و توسعه پایدار

تحولات پنج نسل کشاورزی؛ از کار دستی تا هوش مصنوعی و اتوماسیون

نوع مقاله : مقاله مروری

نویسنده
دانشجوی دکتری تخصصی، گروه مدیریت و توسعه کشاورزی، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع‌طبیعی، دانشگاه تهران، کرج، ایران
چکیده
کشاورزی در طول تاریخ دستخوش تحولات شگرفی شده است که هر نسل آن با پیشرفت‌های فناورانه و تغییرات بنیادین در روش‌های تولید همراه بوده است. مقاله حاضر به بررسی تحولات پنج نسل کشاورزی می‌پردازد؛ از نسل اول که مبتنی بر کار دستی و استفاده از نیروی حیوانات بود، تا نسل پنجم که با ادغام فناوری‌های پیشرفته‌ای مانند هوش مصنوعی، رباتیک، اینترنت اشیا (IoT) و اتوماسیون، کشاورزی را به سطحی کاملاً جدید ارتقا داده است. در نسل اول، کشاورزی به ‌صورت سنتی و با ابزارهای ساده انجام می‌شد، در حالی که نسل دوم با مکانیزاسیون و استفاده از ماشین‌آلات، انقلابی در افزایش بهره‌وری ایجاد کرد. نسل سوم با ورود فناوری‌های زیستی و اصلاح ژنتیکی، انقلاب سبز را رقم زد. نسل چهارم با بهره‌گیری از فناوری‌های دیجیتال و داده‌محور، کشاورزی دقیق و هوشمند را ممکن ساخت. اکنون، نسل پنجم کشاورزی با تمرکز بر همکاری انسان و ماشین، استفاده از هوش مصنوعی و سیستم‌های خودکار، به دنبال ایجاد کشاورزی پایدار، کارآمد و سازگار با محیط زیست است. در مجموع، تحولات پنج‌نسلی کشاورزی نشان‌دهنده حرکت این صنعت به سمت هوشمندتر شدن و کاهش وابستگی به نیروی انسانی است و چالش‌هایی مانند فراهم بودن زیرساخت‌ها، مقرون‌به‌صرفه بودن فناوری برای کشاورزان خرده‌پا و ملاحظات اخلاقی در استفاده از هوش مصنوعی نیز باید مورد توجه قرار گیرند. برای غلبه بر این چالش‌ها، هماهنگی‌های لازم در قالب سرمایه‌گذاری مشترک در تحقیق و توسعه، تدوین استانداردهای بین‌المللی، و طراحی سیاست‌های حمایتی ضروری است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

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

نویسنده English

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.
چکیده English

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..

کلیدواژه‌ها English

Agricultural Evolution
Artificial Intelligence (AI)
Automation
Fifth-Generation Agriculture (5.0)
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
دوره 1، شماره 2
پاییز 1404
صفحه 194-206

  • تاریخ دریافت 11 تیر 1404
  • تاریخ بازنگری 24 تیر 1404
  • تاریخ پذیرش 13 مرداد 1404
  • تاریخ انتشار 01 مهر 1404