Optimizing Agricultural Data Management: A Collaborative Approach through Extreme Programming in Software Development
DOI:
https://doi.org/10.54209/infosains.v14i01.3718Keywords:
Agricultural Data, Software Dev, Extreme ProgrammingAbstract
This research explores the profound transformation of the agricultural sector through the application of advanced technologies, pursuing efficiency and sustainability. The first phase, Data Collection, focuses on identifying data needs for farm management through field surveys and the integration of agricultural sensors. Technologies such as mobile apps and the Internet of Things (IoT) help to efficiently collect high-quality data on soil, weather, and other agricultural factors. The second phase, Application Development, introduces technology integration with Extreme Programming (XP) methods, resulting in adaptive and efficient applications. XP's collaborative and responsive process ensures the successful implementation of the app through thorough functional tests before the official launch. The final phase, User Acceptance Analysis, evaluates user acceptance variables with the Technology Acceptance Model (TAM), highlighting perceived usefulness as a critical factor in users' intention to adopt the application. Implications of the findings include suggestions for app updates focusing on optimizing the user interface to improve perceived usefulness. In discussion, this research blends aspects of technology and farm management, providing an in-depth look at the potential for continuous innovation in improving agricultural productivity. Overall, this research reflects valuable contributions regarding current technological solutions to optimize farm management in the modern era, paving the way for continuous improvements in efficiency and responsiveness to changes in the agricultural cycle
Downloads
References
H. F. Williamson and S. Leonelli, “Accelerating agriculture: Data-intensive plant breeding and the use of genetic gain as an indicator for agricultural research and development,” Stud Hist Philos Sci, vol. 95, pp. 167–176, Oct. 2022, doi: 10.1016/j.shpsa.2022.08.006.
L. C. Hinnou, E. A. R. Obossou, and N. R. A. Adjovi, “Understanding the mechanisms of access and management of agricultural machinery in Benin,” Sci Afr, vol. 15, Mar. 2022, doi: 10.1016/j.sciaf.2022.e01121.
N. Badrzadeh, J. M. V. Samani, M. Mazaheri, and A. Kuriqi, “Evaluation of management practices on agricultural nonpoint source pollution discharges into the rivers under climate change effects,” Science of the Total Environment, vol. 838, Sep. 2022, doi: 10.1016/j.scitotenv.2022.156643.
A. Ashraf and K. Jamil, “Solar-powered irrigation system as a nature-based solution for sustaining agricultural water management in the Upper Indus Basin,” Nature-Based Solutions, vol. 2, p. 100026, Dec. 2022, doi: 10.1016/j.nbsj.2022.100026.
B. Chaudhary and V. Kumar, “Emerging Technological Frameworks for the Sustainable Agriculture and Environmental Management,” Sustainable Horizons, vol. 3. Elsevier B.V., Sep. 01, 2022. doi: 10.1016/j.horiz.2022.100026.
W. Wang, E. Straffelini, A. Pijl, and P. Tarolli, “Sustainable water resource management in steep-slope agriculture,” Geography and Sustainability, vol. 3, no. 3, pp. 214–219, Sep. 2022, doi: 10.1016/j.geosus.2022.07.001.
B. Droppers, I. Supit, R. Leemans, M. T. H. van Vliet, and F. Ludwig, “Limits to management adaptation for the Indus’ irrigated agriculture,” Agric For Meteorol, vol. 321, Jun. 2022, doi: 10.1016/j.agrformet.2022.108971.
E. Daouti, B. Feit, and M. Jonsson, “Agricultural management intensity determines the strength of weed seed predation,” Agric Ecosyst Environ, vol. 339, Nov. 2022, doi: 10.1016/j.agee.2022.108132.
O. Debauche, S. Mahmoudi, P. Manneback, and F. Lebeau, “Cloud and distributed architectures for data management in agriculture 4.0: Review and future trends,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 9. King Saud bin Abdulaziz University, pp. 7494–7514, Oct. 01, 2022. doi: 10.1016/j.jksuci.2021.09.015.
T. Dingsøyr, S. Nerur, V. Balijepally, and N. B. Moe, “A decade of agile methodologies: Towards explaining agile software development,” Journal of Systems and Software, vol. 85, no. 6. 2012. doi: 10.1016/j.jss.2012.02.033.
P. Serrador and J. K. Pinto, “Does Agile work? - A quantitative analysis of agile project success,” International Journal of Project Management, vol. 33, no. 5, 2015, doi: 10.1016/j.ijproman.2015.01.006.
R. Santos et al., “A Comparative Analysis of Agile Teamwork Quality Instruments in Agile Software Development: A Qualitative Approach”, doi: 10.18293/DMSVIVA2023-217.
S. Al-Saqqa, S. Sawalha, and H. Abdelnabi, “Agile software development: Methodologies and trends,” International Journal of Interactive Mobile Technologies, vol. 14, no. 11, 2020, doi: 10.3991/ijim.v14i11.13269.
T. Dingsoeyr, D. Falessi, and K. Power, “Agile Development at Scale: The Next Frontier,” IEEE Software, vol. 36, no. 2. IEEE Computer Society, pp. 30–38, Mar. 01, 2019. doi: 10.1109/MS.2018.2884884.
F. Almeida, J. Simões, and S. Lopes, “Exploring the Benefits of Combining DevOps and Agile,” Future Internet, vol. 14, no. 2, Feb. 2022, doi: 10.3390/fi14020063.
A. Akhtar, B. Bakhtawar, and S. Akhtar, “EXTREME PROGRAMMING VS SCRUM: A COMPARISON OF AGILE MODELS,” International Journal of Technology, Innovation and Management (IJTIM), vol. 2, p. 2022, doi: 10.54489/ijtim.v2i1.77.
R. Hasan, A.- Ta, and R. Razali, “Prioritizing Requirements in Agile Development : A Conceptual Framework,” Procedia Technology, vol. 11, no. Iceei, pp. 733–739, 2013, doi: 10.1016/j.protcy.2013.12.252.
A. Mishra and Y. I. Alzoubi, “Structured software development versus agile software development: a comparative analysis,” International Journal of System Assurance Engineering and Management, Aug. 2023, doi: 10.1007/s13198-023-01958-5.
M. S. Khan, A. W. Khan, F. Khan, M. A. Khan, and T. K. Whangbo, “Critical Challenges to Adopt DevOps Culture in Software Organizations: A Systematic Review,” IEEE Access, vol. 10, pp. 14339–14349, 2022, doi: 10.1109/ACCESS.2022.3145970.
M. Ahmed, S. U. R. Khan, and K. A. Alam, “An NLP-based quality attributes extraction and prioritization framework in Agile-driven software development,” Automated Software Engineering, vol. 30, no. 1, May 2023, doi: 10.1007/s10515-022-00371-9.
T. Clement, N. Kemmerzell, M. Abdelaal, and M. Amberg, “XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process,” Machine Learning and Knowledge Extraction, vol. 5, no. 1. MDPI, pp. 78–108, Mar. 01, 2023. doi: 10.3390/make5010006.
V. Pérez-Piqueras, P. Bermejo, and J. A. Gámez, “ProjectION: A computational intelligence-based tool for decision support in agile software development projects,” 2023, doi: 10.22541/au.167575146.62025490/v1.
J. Udvaros, N. Forman, and S. M. Avornicului, “Agile Storyboard and Software Development Leveraging Smart Contract Technology in Order to Increase Stakeholder Confidence,” Electronics (Switzerland), vol. 12, no. 2, Jan. 2023, doi: 10.3390/electronics12020426.
J. Leong, K. May Yee, O. Baitsegi, L. Palanisamy, and R. K. Ramasamy, “Hybrid Project Management between Traditional Software Development Lifecycle and Agile Based Product Development for Future Sustainability,” Sustainability, vol. 15, no. 2, p. 1121, Jan. 2023, doi: 10.3390/su15021121.
V. Mezhuyev, M. Al-Emran, M. A. Ismail, L. Benedicenti, and D. A. P. Chandran, “The Acceptance of Search-Based Software Engineering Techniques: An Empirical Evaluation Using the Technology Acceptance Model,” IEEE Access, vol. 7, pp. 101073–101085, 2019, doi: 10.1109/access.2019.2917913.
T. Huikkola, M. Kohtamäki, R. Rabetino, H. Makkonen, and P. Holtkamp, “Overcoming the challenges of smart solution development: Co-alignment of processes, routines, and practices to manage product, service, and software integration,” Technovation, vol. 118, Dec. 2022, doi: 10.1016/j.technovation.2021.102382.
D. J. C. Sihombing, “Analysis and development of the ProTrack application: construction timeline management using Extreme Programming Methodology,” Online, 2023.
J. Chen, T. Yu, L. Yin, J. Tang, and H. Wang, “A unified time scale intelligent control algorithm for microgrid based on extreme dynamic programming,” CSEE Journal of Power and Energy Systems, vol. 6, no. 3, pp. 583–590, Sep. 2020, doi: 10.17775/CSEEJPES.2019.00100.
