| Peer-Reviewed

Reshaping of the Future Farming: From Industry 4.0 Toward Agriculture 4.0

Received: 22 December 2022     Accepted: 25 January 2023     Published: 10 June 2023
Views:       Downloads:
Abstract

Contemporary agriculture faces many challenges, most notably the large and continuous increase in population numbers, which requires the greater provision of agricultural products to meet people's need for food. There are also other challenges such as global climate change, which has recently increased inefficiency due to droughts, desertification, irrigation water decreasing, increased soil contamination, plant diseases, heat waves, floods, and water salinity, causing many agricultural problems. The agricultural industry needs to invest in new techniques and infrastructure that enable it to transform into a smart industry capable of responding to these challenges through lean operations supported by industrial digital technologies (IDTs) to maintain efficiency, sustainability, and quality. The industry 4.0 strategy has been widely adopted by the manufacturing industry, enabling the manufacturing sector to achieve the enhanced optimization, efficiency, responsiveness, and autonomy premises of the digitalization strategy. This paper discusses the digitization of agriculture "from industry 4.0 towards agriculture 4.0," which relies on Internet of Things (IoT) technologies, artificial neural networks (ANN), AI, and fuzzy logic to make a quantum leap, in the future of agriculture sector. Internet of Things devices collect data from the devices or sensors, analyse, process, and transfer it, in addition to making the right decisions, without human intervention. IOT also provides the basic communications infrastructure that is used to connect smart devices, sensors, and UAVs to mobile devices by using the Internet. These processes will lead to many services, such as collecting and analysing the information, pattern recognition, and independent decision-making based on artificial intelligence added to the current agricultural automation. These technologies will lead to a revolution in the field of agriculture, which is probably one of the most inefficient sectors today.

Published in American Journal of Applied Scientific Research (Volume 9, Issue 2)
DOI 10.11648/j.ajasr.20230902.14
Page(s) 62-71
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Smart Farming, Agriculture 4.0, Industry 4.0, Artificial Intelligence, Fuzzy Control, Internet of Things (IOT), Unmanned Aerial Vehicles (UAV)

References
[1] Abbasi, Rabiya, et al. “The Digitization of Agricultural Industry – a Systematic Literature Review on Agriculture 4.0.” Smart Agricultural Technology, vol. 2, no. CC BY-NC-ND, 2 Dec. 2022, p. 100042. Published by Elsevier B. V., 10.1016/j.atech.2022.100042.
[2] Arif, C., Mizoguchi, M., Setiawan, B. I., Doi, R., (2012), “Estimation of soil moisture in paddy field using Artificial Neural Networks”, International Journal of Advanced Research in Artificial Intelligence. 1 (1), 17–21.
[3] Badrun, B. and Manaf, M., 2021, September. The Development of Smart Irrigation System with IoT, Cloud, and Big Data. In IOP Conference Series: Earth and Environmental Science (Vol. 830, No. 1, p. 012009). IOP Publishing.
[4] Bernhardt, Heinz, et al. “Challenges for Agriculture through Industry 4.0.” Agronomy, vol. 11, no. 10, 27 Sept. 2021, p. 1935, 10.3390/agronomy11101935. Accessed 22 June 2022.
[5] Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2022). Internet of things (IOT) and Agricultural Unmanned Aerial Vehicles (uavs) in Smart farming: A comprehensive review. Internet of Things, 18, 100187. https://doi.org/10.1016/j.iot.2020.100187
[6] Daponte, P., De Vito, L., Glielmo, L., Iannelli, L., Liuzza, D., Picariello, F., & Silano, G. (2019). A review on the use of drones for Precision Agriculture. IOP Conference Series: Earth and Environmental Science, 275 (1), 012022. https://doi.org/10.1088/1755-1315/275/1/012022
[7] De clerq. M, Vats. A and Biel. A (2018) 'Agiculture 4.0: Future of farming Technology. World coverment summit (Oliver. W), 1 (1.2) PP 5-6.
[8] Elijah, Olakunle, et al. “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges.” IEEE Internet of Things Journal, vol. 5, no. 5, Oct. 2018, pp. 3758–3773, 10.1109/jiot.2018.2844296.
[9] Khatoon, S., Rawat, A., Bhople, S. and Dwivedi, P., Robotic Technology: Fate of Agriculture in Future Scenario. Krishi Udyan Darpan. Gliever, C., Slaughter, D. C., (2001), “Crop verses weed recognition with artificial neural networks” ASAE paper. 01-3104 (2001), 1–12.
[10] Gutiérrez-Rodríguez, A., Décima, M., Popp, B. N., & Landry, M. R. (2014). Isotopic invisibility of protozoan trophic steps in marine food webs. Limnology and Oceanography, 59 (5), 1590–1598. https://doi.org/10.4319/lo.2014.59. 5.1590
[11] Haiyan, S., & Yong, H. (2015). Crop nutrition diagnosis expert system based on Artificial Neural Networks. Third International Conference on Information Technology and Applications (ICITA'05). https://doi.org/10.1109/icita.2005.108
[12] Hinnell, A. C., Lazarovitch, N., Furman, A., Poulton, M., Warrick, A. W, (2010), “Neuro-drip: estimation of subsurface wetting patterns for drip irrigation using neural networks”, Irrig. Sci. 28, 535–544.
[13] Iliev O. L., P. Sazdov, A. Zakeri (2014), Fuzzy logic based Control for Protected Cultivation, Journal Management of Environmental Quality, Vol. 25 Issue 1, 83-92.
[14] Iliev O. L., Zakeri A., K. M. Naing and N. Venkateshaiah, (2017), “Greenhouse Cultivation Control - Fuzzy Logic based Approach” 2nd International Conference on Advancement in Engineering, Applied Science and Management (ICAEASM- 2017), Osman University, Hyderabad, India.
[15] Jelle, B. (Ed.). (2012). The futures of Agriculture - GFAR. What are the likely developments in world agriculture towards 2050? Retrieved January 6, 2023, from https://www.gfar.net/ sites/default/files/files/Jelle%20Bruinsma_FAO_Brief%2038.pdf
[16] Jha, K., Doshi, A., & Patel, P. (2018). Intelligent irrigation system using Artificial Intelligence and machine learning: A comprehensive review. International Journal of Advanced Research, 6 (10), 1493–1502. https://doi.org/10.21474/ijar01/7959
[17] Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1–12. https://doi.org/10.1016/j.aiia.2019.05.004
[18] Kumar, G. and Narayan, B., 2014. Prevention of infection in the treatment of one thousand and twenty-five open fractures of long bones. Retrospective and prospective analyses. Classic Papers in Orthopaedics, pp. 527-530.
[19] Klerkx, Laurens, et al. “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, Dec. 2022, p. 100315, 10.1016/j.njas.2019.100315.
[20] Kodali, R. K., Jain, V., & Karagwal, S. (2016). IOT based Smart Greenhouse. 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). https://doi.org/10.1109/r10-htc.2016.7906846
[21] M. Roopaei, P. Rad and K. -K. R. Choo, "Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging," in IEEE Cloud Computing, vol. 4, no. 1, pp. 10-15, Jan.-Feb. 2017, doi: 10.1109/MCC.2017.5.
[22] Maier, H. R., Dandy, G. C., (2000), “Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications“, Environmental Modeling & Software 101–124.
[23] Malavade, V. N. and Akulwar, P. K., 2016. Role of IoT in agriculture. IOSR Journal of Computer Engineering, 2016, pp. 2278-0661.
[24] Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2019). Review—machine learning techniques in wireless sensor network based Precision Agriculture. Journal of The Electrochemical Society, 167 (3), 037522. https://doi.org/10.1149/2.0222003jes
[25] Zhang, L., Dabipi, I. K. and Brown, W. L, (2018), "Internet of Things Applications for Agriculture". In, Internet of Things A to Z: Technologies and Applications, Q. Hassan (Ed.),
[26] Navulur, S., S. C. S. Sastry, A., & N. Giri Prasad, M. (2017). Agricultural management through wireless sensors and internet of things. International Journal of Electrical and Computer Engineering (IJECE), 7 (6), 3492. https://doi.org/10.11591/ijece.v7i6.pp3492-3499
[27] Ravichandran, G., Koteshwari, R. S., (2016), “Agricultural crop predictor and advisor using ANN for smartphones”. IEEE 1–6.
[28] Rubio, F., Valero, F., & Llopis-Albert, C. (2019). A review of Mobile Robots: Concepts, methods, theoretical framework, and applications. International Journal of Advanced Robotic Systems, 16 (2), 172988141983959. https://doi.org/10.1177/1729881419839596
[29] Sannakki, S. S., Rajpurohit, V. S., & Nargund, V. B. (2013). SVM-DSD: SVM Based Diagnostic System for the detection of pomegranate leaf diseases. Advances in Intelligent Systems and Computing, 715–720. https://doi.org/10.1007/978-81-322-0740-5_85
[30] Shahzadi, R., Tausif, M., Ferzund, J., Suryani, M. A., (2016) “Internet of things based expert system for smart agriculture”. Int. J. Adv. Comput. Sci. Appl. 7 (9), 341–350.
[31] Telukdarie, A., and M. N. Sishi. “Enterprise Definition for Industry 4.0.” IEEE Xplore, 1 Dec. 2018, ieeexplore.ieee.org/abstract/document/8607642/. Accessed 15 Mar. 2022.
[32] Tremblay, N., Bouroubi, M. Y., Panneton, B., Guillaume, S., Vigneault, P., & Bélec, C. (2010). Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features. Precision Agriculture, 11 (6), 621–635. https://doi.org/10.1007/s11119-010-9188-z
[33] Valdés-Vela, M., Abrisqueta, I., Conejero, W., Vera, J., & Ruiz-Sánchez, M. C. (2015). Soft computing applied to stem water potential estimation: A fuzzy rule based approach. Computers and Electronics in Agriculture, 115, 150–160. https://doi.org/10.1016/j.compag.2015.05.019
[34] Y. Liu, X. Ma, L. Shu, G. P. Hancke and A. M. Abu-Mahfouz, "From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges," in IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4322-4334, June 2021, doi: 10.1109/TII.2020.3003910.
[35] Yang, F., & Gu, S. (2021). Industry 4.0, a revolution that requires technology and National Strategies. Complex & Intelligent Systems, 7 (3), 1311–1325. https://doi.org/10.1007/s40747-020-00267-9
[36] Kim, Y., Evans, R. G. and Iversen, W. M., 2008. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE transactions on instrumentation and measurement, 57 (7), pp. 1379-1387.
[37] Tilva, V., Patel, J. and Bhatt, C., 2013, November. Weather based plant diseases forecasting using fuzzy logic. In 2013 Nirma University International Conference on Engineering (NUiCONE) (pp. 1-5). IEEE.
Cite This Article
  • APA Style

    Bushara Ali, Ahmad Zakeri, Anamarija Llieva, Oliver Iliev. (2023). Reshaping of the Future Farming: From Industry 4.0 Toward Agriculture 4.0. American Journal of Applied Scientific Research, 9(2), 62-71. https://doi.org/10.11648/j.ajasr.20230902.14

    Copy | Download

    ACS Style

    Bushara Ali; Ahmad Zakeri; Anamarija Llieva; Oliver Iliev. Reshaping of the Future Farming: From Industry 4.0 Toward Agriculture 4.0. Am. J. Appl. Sci. Res. 2023, 9(2), 62-71. doi: 10.11648/j.ajasr.20230902.14

    Copy | Download

    AMA Style

    Bushara Ali, Ahmad Zakeri, Anamarija Llieva, Oliver Iliev. Reshaping of the Future Farming: From Industry 4.0 Toward Agriculture 4.0. Am J Appl Sci Res. 2023;9(2):62-71. doi: 10.11648/j.ajasr.20230902.14

    Copy | Download

  • @article{10.11648/j.ajasr.20230902.14,
      author = {Bushara Ali and Ahmad Zakeri and Anamarija Llieva and Oliver Iliev},
      title = {Reshaping of the Future Farming: From Industry 4.0 Toward Agriculture 4.0},
      journal = {American Journal of Applied Scientific Research},
      volume = {9},
      number = {2},
      pages = {62-71},
      doi = {10.11648/j.ajasr.20230902.14},
      url = {https://doi.org/10.11648/j.ajasr.20230902.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajasr.20230902.14},
      abstract = {Contemporary agriculture faces many challenges, most notably the large and continuous increase in population numbers, which requires the greater provision of agricultural products to meet people's need for food. There are also other challenges such as global climate change, which has recently increased inefficiency due to droughts, desertification, irrigation water decreasing, increased soil contamination, plant diseases, heat waves, floods, and water salinity, causing many agricultural problems. The agricultural industry needs to invest in new techniques and infrastructure that enable it to transform into a smart industry capable of responding to these challenges through lean operations supported by industrial digital technologies (IDTs) to maintain efficiency, sustainability, and quality. The industry 4.0 strategy has been widely adopted by the manufacturing industry, enabling the manufacturing sector to achieve the enhanced optimization, efficiency, responsiveness, and autonomy premises of the digitalization strategy. This paper discusses the digitization of agriculture "from industry 4.0 towards agriculture 4.0," which relies on Internet of Things (IoT) technologies, artificial neural networks (ANN), AI, and fuzzy logic to make a quantum leap, in the future of agriculture sector. Internet of Things devices collect data from the devices or sensors, analyse, process, and transfer it, in addition to making the right decisions, without human intervention. IOT also provides the basic communications infrastructure that is used to connect smart devices, sensors, and UAVs to mobile devices by using the Internet. These processes will lead to many services, such as collecting and analysing the information, pattern recognition, and independent decision-making based on artificial intelligence added to the current agricultural automation. These technologies will lead to a revolution in the field of agriculture, which is probably one of the most inefficient sectors today.},
     year = {2023}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Reshaping of the Future Farming: From Industry 4.0 Toward Agriculture 4.0
    AU  - Bushara Ali
    AU  - Ahmad Zakeri
    AU  - Anamarija Llieva
    AU  - Oliver Iliev
    Y1  - 2023/06/10
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajasr.20230902.14
    DO  - 10.11648/j.ajasr.20230902.14
    T2  - American Journal of Applied Scientific Research
    JF  - American Journal of Applied Scientific Research
    JO  - American Journal of Applied Scientific Research
    SP  - 62
    EP  - 71
    PB  - Science Publishing Group
    SN  - 2471-9730
    UR  - https://doi.org/10.11648/j.ajasr.20230902.14
    AB  - Contemporary agriculture faces many challenges, most notably the large and continuous increase in population numbers, which requires the greater provision of agricultural products to meet people's need for food. There are also other challenges such as global climate change, which has recently increased inefficiency due to droughts, desertification, irrigation water decreasing, increased soil contamination, plant diseases, heat waves, floods, and water salinity, causing many agricultural problems. The agricultural industry needs to invest in new techniques and infrastructure that enable it to transform into a smart industry capable of responding to these challenges through lean operations supported by industrial digital technologies (IDTs) to maintain efficiency, sustainability, and quality. The industry 4.0 strategy has been widely adopted by the manufacturing industry, enabling the manufacturing sector to achieve the enhanced optimization, efficiency, responsiveness, and autonomy premises of the digitalization strategy. This paper discusses the digitization of agriculture "from industry 4.0 towards agriculture 4.0," which relies on Internet of Things (IoT) technologies, artificial neural networks (ANN), AI, and fuzzy logic to make a quantum leap, in the future of agriculture sector. Internet of Things devices collect data from the devices or sensors, analyse, process, and transfer it, in addition to making the right decisions, without human intervention. IOT also provides the basic communications infrastructure that is used to connect smart devices, sensors, and UAVs to mobile devices by using the Internet. These processes will lead to many services, such as collecting and analysing the information, pattern recognition, and independent decision-making based on artificial intelligence added to the current agricultural automation. These technologies will lead to a revolution in the field of agriculture, which is probably one of the most inefficient sectors today.
    VL  - 9
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • School of Engineering, University of Wolverhampton, Telford, United Kingdom

  • School of Engineering, University of Wolverhampton, Telford, United Kingdom

  • Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Skopje, Republic of Macedonia

  • Faculty of Information and Communication Technologies, FON University, Skopje, Republic of Macedonia

  • Sections