Güresci, E.; Tekinerdogan, B.; Babur, Ö.; Liu, Q. Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions. Preprints2024, 2024092025. https://doi.org/10.20944/preprints202409.2025.v1
APA Style
Güresci, E., Tekinerdogan, B., Babur, Ö., & Liu, Q. (2024). Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions. Preprints. https://doi.org/10.20944/preprints202409.2025.v1
Chicago/Turabian Style
Güresci, E., Önder Babur and Qingzhi Liu. 2024 "Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions" Preprints. https://doi.org/10.20944/preprints202409.2025.v1
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.
Keywords
no code development platform; low code development platform; precision agriculture; software engineering; domain analysis
Subject
Computer Science and Mathematics, Software
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
