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Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions

Author

Listed:
  • Emin Guresci

    (Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands)

  • Bedir Tekinerdogan

    (Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands)

  • Önder Babur

    (Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
    Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands)

  • Qingzhi Liu

    (Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands)

Abstract

Low-Code Development Platforms (LCDPs) empower users to create and deploy custom software with little to no programming. These platforms streamline development, offering benefits like faster time-to-market, reduced technical barriers, and broader participation in software creation, even for those without traditional coding skills. This study explores the application of LCDPs in Precision Agriculture (PA) through a systematic literature review (SLR). By analyzing the general characteristics and challenges of LCDPs, alongside insights from existing PA research, we assess their feasibility and potential impact in agricultural contexts. Our findings suggest that LCDPs can enable farmers and agricultural professionals to create tailored applications for real-time monitoring, data analysis, and automation, enhancing farming efficiency. However, challenges such as scalability, extensibility, data security, and integration with complex IoT systems must be addressed to fully realize the benefits of LCDPs in PA. This study contributes to the growing knowledge base in agricultural technology, offering valuable insights for researchers, practitioners, and policymakers looking to leverage LCDPs for sustainable and efficient farming practices.

Suggested Citation

  • Emin Guresci & Bedir Tekinerdogan & Önder Babur & Qingzhi Liu, 2024. "Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions," Land, MDPI, vol. 13(11), pages 1-31, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1758-:d:1506865
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    References listed on IDEAS

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    1. López-Riquelme, J.A. & Pavón-Pulido, N. & Navarro-Hellín, H. & Soto-Valles, F. & Torres-Sánchez, R., 2017. "A software architecture based on FIWARE cloud for Precision Agriculture," Agricultural Water Management, Elsevier, vol. 183(C), pages 123-135.
    2. Tatiana Fomenko & Marina Bilotserkovets & Tetiana Klochkova & Olena Statsenko & Alina Sbruieva & Olena Kozlova & Dmytro Kozlov, 2020. "Overcoming Barriers in Intercultural Communication: A Case Study on Agricultural Idioms in English, Ukrainian and Chinese," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 9, November.
    3. Verdouw, Cor & Tekinerdogan, Bedir & Beulens, Adrie & Wolfert, Sjaak, 2021. "Digital twins in smart farming," Agricultural Systems, Elsevier, vol. 189(C).
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