Simulation-Based Evaluation of Offline Web Mapping for Agricultural Land Inventory: Usability and Data Quality

Sanriomi  Sintaro; Saroyo; Ricardo Gianluigi  Tindi

doi:10.35791/jat.v7i3.70505

Authors

Sanriomi Sintaro Information Systems Study Program, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University
Saroyo Biology Study Program, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University
Ricardo Gianluigi Tindi Management Study Program, Faculty of Economics and Business, Sam Ratulangi University

DOI:

https://doi.org/10.35791/jat.v7i3.70505

Keywords:

Agricultural inventory, Data quality, Offline web mapping, Simulation, Usability

Abstract

Agricultural land inventories require reliable spatial records, accessible software, and consistent data validation. This study assessed AgriMap Field version 1.2, a self-contained offline web mapping application, through a computational simulation designed to examine its evaluation procedures before field deployment. A structured simulation dataset comprised 30 hypothetical user profiles, 120 task attempts across four inventory activities, 30 ten-item System Usability Scale (SUS) response sets, 30 agricultural plot records, and paired geographic coordinates. Task completion was assessed using recorded success and a stricter definition requiring completion within the specified time without assistance. SUS scoring and internal consistency were recalculated, and coordinate-pair distances were independently verified using the haversine formula. Recorded task success was 88.3% (106/120), while strict independent success was 80.0% (96/120); spatial location retrieval had the lowest strict success (70.0%). The simulated SUS mean was 73.0 (standard deviation 9.01), with Cronbach's alpha of 0.774. All 30 simulated inventory records contained the six mandatory fields, and the mean distance between paired coordinates was 6.43 m; 25 of 30 pairs were separated by less than 10 m. Separately, all 61 predefined software regression checks passed. These findings support the internal consistency of the evaluation framework and identify mapping navigation as a priority for subsequent testing. Because the user responses and geographic reference records were simulated, these measures do not represent observed farmer usability or independently measured field positional accuracy

References

Anderson, E., Lybbert, T. J., Shenoy, A., Singh, R., & Stein, D. (2024). Does survey mode matter? Comparing in-person and phone agricultural surveys in India. Journal of Development Economics, 166, 103199. https://doi.org/10.1016/j.jdeveco.2023.103199

Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the System Usability Scale. International Journal of Human–Computer Interaction, 24(6), 574–594. https://doi.org/10.1080/10447310802205776

Basir, M. S., Zhang, Y., Buckmaster, D., Raturi, A., & Krogmeier, J. V. (2025). Meta Ag: An automatic agricultural contextual metadata collection app. Smart Agricultural Technology, 12, 101073. https://doi.org/10.1016/j.atech.2025.101073

Brodeur, J., Coetzee, S., Danko, D., Garcia, S., & Hjelmager, J. (2019). Geographic information metadata—An outlook from the international standardization perspective. ISPRS International Journal of Geo-Information, 8(6), 280. https://doi.org/10.3390/ijgi8060280

Chamorro-Padial, J., García, R., & Gil, R. (2024). A systematic review of open data in agriculture. Computers and Electronics in Agriculture, 219, 108775. https://doi.org/10.1016/j.compag.2024.108775

Faulkner, L. (2003). Beyond the five-user assumption: Benefits of increased sample sizes in usability testing. Behavior Research Methods, Instruments, & Computers, 35(3), 379–383. https://doi.org/10.3758/BF03195514

Krisnawijaya, N. N. K., Tekinerdogan, B., Catal, C., van der Tol, R., & Herdiyeni, Y. (2025). Implementing FAIR principles in data management systems: A multi-case study in precision farming. Computers and Electronics in Agriculture, 230, 109855. https://doi.org/10.1016/j.compag.2024.109855

Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267. https://doi.org/10.1525/collabra.33267

Lamaega, J. C. E., Paat, F. J., Sintaro, S. S., & Saroinsong, D. (2026). Development and performance evaluation of a low-cost, energy-efficient air fryer prototype using incandescent bulb heating for oil-free food processing. Jurnal Agroekoteknologi Terapan, 7(1), 38–51. https://doi.org/10.35791/jat.v7i1.66252

Lamb, J. R., Araújo, E. C., Bazzi, C. L., & Maggi, M. F. (2022). Sampling in precision agriculture: A software for navigation in field activities. Sustainable Computing: Informatics and Systems, 36, 100804. https://doi.org/10.1016/j.suscom.2022.100804

Lewis, J. R. (2018). The System Usability Scale: Past, present, and future. International Journal of Human–Computer Interaction, 34(7), 577–590. https://doi.org/10.1080/10447318.2018.1455307

Liu, S., Li, B., Chen, Y., & Cai, Z. (2024). Establishing a generic geographic information collection platform for heterogeneous data. Applied Sciences, 14(13), 5416. https://doi.org/10.3390/app14135416

Minallah, N., Khan, W., Zeeshan, M., & Ahmad, T. (2024). GeoSurvey: A cloud-based mobile app for efficient land surveys and big data collection. Software Impacts, 19, 100620. https://doi.org/10.1016/j.simpa.2024.100620

Moinard, S., Oger, B., Brunel, G., Ducanchez, A., Crestey, T., Faure, P., Pichon, L., & Tisseyre, B. (2025). A collaborative and low cost acquisition chain for collecting spatialized observations in agriculture. Computers and Electronics in Agriculture, 239, 111113. https://doi.org/10.1016/j.compag.2025.111113

Nowak, M. M., Dziób, K., Ludwisiak, Ł., & Chmiel, J. (2020). Mobile GIS applications for environmental field surveys: A state of the art. Global Ecology and Conservation, 23, e01089. https://doi.org/10.1016/j.gecco.2020.e01089

Senaratne, H., Mobasheri, A., Ali, A. L., Capineri, C., & Haklay, M. (2017). A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science, 31(1), 139–167. https://doi.org/10.1080/13658816.2016.1189556

Tenorio, F. A. M., Rattalino Edreira, J. I., Monzon, J. P., Aramburu-Merlos, F., Dobermann, A., Gruere, A., Brihet, J. M., Gayo, S., Conley, S., Mourtzinis, S., Mashingaidze, N., Sananka, A., Aston, S., Ojeda, J. J., & Grassini, P. (2024). Filling the agronomic data gap through a minimum data collection approach. Field Crops Research, 308, 109278. https://doi.org/10.1016/j.fcr.2024.109278

Zhou, C., Lee, W. S., Zhang, S., Liburd, O. E., Pourreza, A., Schueller, J. K., & Ampatzidis, Y. (2024). A smartphone application for site-specific pest management based on deep learning and spatial interpolation. Computers and Electronics in Agriculture, 218, 108726. https://doi.org/10.1016/j.compag.2024.108726