From Field Geometry to Jewelry Interfaces: Multimodal AI-Assisted Shape Translation and Evidence-Grounded Agricultural Storytelling

Authors

  • Evelyn Liu Design and Technology, Parsons School of Design, NY, USA
  • Lumeng Han International Agricultural Development, University of California, Davis, CA, USA

DOI:

https://doi.org/10.51903/ijgd.v3i1.4276

Keywords:

agricultural field geometry, computational jewelry design, UI/UX design, visual communication, design provenance, sustainability communication

Abstract

This study develops a field-to-ornament framework connecting agricultural field geometry, computational jewelry design, and evidence-grounded interface storytelling. The main design pipeline uses 350 Fields2Benchmark polygons from the Netherlands, Lithuania, and Estonia as authoritative sources for contour translation. Five abstraction levels are evaluated through geometric similarities and a Geometry Preservation Score (GPS). The Balanced setting retains mean GPS 93.46, compared with 95.06 for Subtle and 88.06 for Abstract. The non-Trace ranking persists across 15,000 sampled weight–scale combinations, although orientation-only weighting reverses Subtle and Balanced. Balanced remains a heuristic demonstration choice without evidence of aesthetic preference. Optional image-recovery experiments assess the limitations of deriving contours when vector geometry is unavailable. On 53 overlay-suppressed annotated map plates, Random Forest achieves mean IoU 0.5417, boundary F1 0.2119, and absolute area error 53.56%. A separate 350-tile AI4Boundaries subset yields modest labelled-extent recovery on 70 aerial test tiles; contour smoothing does not correct prediction errors. Four provenance-card designs are instantiated for 30 fields, producing 120 deterministic audits. Balanced disclosure scores 20.00 and the unsupported minimal claim 100.00 under the proposed overclaim rubric. Sensitivity and controlled content–layout comparisons characterize the rubric's behavior, without measuring comprehension or trust. The framework documents how field geometry can inform two-dimensional ornament concepts while distinguishing design provenance from claims about crop identity, ownership, farming practice, material origin, or environmental benefit.

References

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper 3, pp. 1-13). Association for Computing Machinery. https://doi.org/10.1145/3290605.3300233

Atkinson, L., & Rosenthal, S. (2014). Signaling the green sell: The influence of eco-label source, argument specificity, and product involvement on consumer trust. Journal of Advertising, 43(1), 33-45. https://doi.org/10.1080/00913367.2013.834803

Beilin, R. (2005). Photo-elicitation and the agricultural landscape: “Seeing” and “telling” about farming, community and place. Visual Studies, 20(1), 56-68. https://doi.org/10.1080/14725860500064904

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

Caetano, I., Santos, L., & Leitão, A. (2020). Computational design in architecture: Defining parametric, generative, and algorithmic design. Frontiers of Architectural Research, 9(2), 287-300. https://doi.org/10.1016/j.foar.2019.12.008

Chaikin, G. M. (1974). An algorithm for high-speed curve generation. Computer Graphics and Image Processing, 3(4), 346-349. https://doi.org/10.1016/0146-664X(74)90028-8

Crisan, A., Drouhard, M., Vig, J., & Rajani, N. (2022). Interactive model cards: A human-centered approach to model documentation. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 427-439). Association for Computing Machinery. https://doi.org/10.1145/3531146.3533108

d'Andrimont, R., Claverie, M., Kempeneers, P., Muraro, D., Yordanov, M., Peressutti, D., Batič, M., & Waldner, F. (2023). AI4Boundaries: An open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography. Earth System Science Data, 15(1), 317-329. https://doi.org/10.5194/essd-15-317-2023

de Freitas Netto, S. V., Sobral, M. F. F., Ribeiro, A. R. B., & Soares, G. R. da L. (2020). Concepts and forms of greenwashing: A systematic review. Environmental Sciences Europe, 32(1), Article 19. https://doi.org/10.1186/s12302-020-0300-3

Delmas, M. A., & Burbano, V. C. (2011). The drivers of greenwashing. California Management Review, 54(1), 64-87. https://doi.org/10.1525/cmr.2011.54.1.64

Di Nicolantonio, M., Rossi, E., & Stella, P. (2020). Generative design for printable mass customization jewelry products. In M. Di Nicolantonio, E. Rossi, & T. Alexander (Eds.), Advances in additive manufacturing, modeling systems and 3D prototyping (pp. 143-152). Springer. https://doi.org/10.1007/978-3-030-20216-3_14

Douglas, D. H., & Peucker, T. K. (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization, 10(2), 112-122. https://doi.org/10.3138/FM57-6770-U75U-7727

Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021). Expanding explainability: Towards social transparency in AI systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Article 82, pp. 1-19). Association for Computing Machinery. https://doi.org/10.1145/3411764.3445188

Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92. https://doi.org/10.1145/3458723

Gobster, P. H., Nassauer, J. I., Daniel, T. C., & Fry, G. (2007). The shared landscape: What does aesthetics have to do with ecology? Landscape Ecology, 22(7), 959-972. https://doi.org/10.1007/s10980-007-9110-x

Graesser, J., & Ramankutty, N. (2017). Detection of cropland field parcels from Landsat imagery. Remote Sensing of Environment, 201, 165-180. https://doi.org/10.1016/j.rse.2017.08.027

Jeong, J., Park, H., Lee, Y., Kang, J., & Chun, J. (2021). Developing parametric design fashion products using 3D printing technology. Fashion and Textiles, 8(1), Article 22. https://doi.org/10.1186/s40691-021-00247-8

Kielarova, S. W., Pradujphongphet, P., & Bohez, E. L. J. (2013). An approach of generative design system: Jewelry design application. In 2013 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1329-1333). IEEE. https://doi.org/10.1109/IEEM.2013.6962626

Kielarova, S. W., Pradujphongphet, P., & Bohez, E. L. J. (2015). New interactive-generative design system: Hybrid of shape grammar and evolutionary design-An application of jewelry design. In Y. Tan, Y. Shi, F. Buarque, A. Gelbukh, S. Das, & A. Engelbrecht (Eds.), Advances in swarm and computational intelligence (pp. 302-313). Springer. https://doi.org/10.1007/978-3-319-20466-6_33

Kizilcec, R. F. (2016). How much information? Effects of transparency on trust in an algorithmic interface. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2390-2395). Association for Computing Machinery. https://doi.org/10.1145/2858036.2858402

Li, Y. (2024). Findable then explainable: Retrieval–summary integration for code intelligence on a lightweight CodeSearchNet subset. Journal of Advanced Computing Systems, 4(7), 65–82. https://doi.org/10.69987/JACS.2024.40706

Liao, Q. V., Subramonyam, H., Wang, J., & Wortman Vaughan, J. (2023). Designerly understanding: Information needs for model transparency to support design ideation for AI-powered user experience. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Article 9, pp. 1-21). Association for Computing Machinery. https://doi.org/10.1145/3544548.3580652

Lyon, T. P., & Montgomery, A. W. (2015). The means and end of greenwash. Organization & Environment, 28(2), 223-249. https://doi.org/10.1177/1086026615575332

Mier, G., Casado Faulí, A. M., Valente, J., & de Bruin, S. (2024). Fields2Benchmark dataset (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14524735

Mier, G., Valente, J., & de Bruin, S. (2023). Fields2Cover: An open-source coverage path planning library for unmanned agricultural vehicles. IEEE Robotics and Automation Letters, 8(4), 2166-2172. https://doi.org/10.1109/LRA.2023.3248439

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220-229). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287596

Nie, J., & Zheng, D. (2023). Ambiguity-aware HDFS log anomaly detection with retrieval-augmented failure narratives and selective refusal. Journal of Advanced Computing Systems, 3(1), 66–80. https://doi.org/10.69987/JACS.2023.30105

Ode, Å., Tveit, M. S., & Fry, G. (2008). Capturing landscape visual character using indicators: Touching base with landscape aesthetic theory. Landscape Research, 33(1), 89-117. https://doi.org/10.1080/01426390701773854

Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66. https://doi.org/10.1109/TSMC.1979.4310076

Parguel, B., Benoît-Moreau, F., & Larceneux, F. (2011). How sustainability ratings might deter “greenwashing”: A closer look at ethical corporate communication. Journal of Business Ethics, 102(1), 15-28. https://doi.org/10.1007/s10551-011-0901-2

Persello, C., Tolpekin, V. A., Bergado, J. R., & de By, R. A. (2019). Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. Remote Sensing of Environment, 231, Article 111253. https://doi.org/10.1016/j.rse.2019.111253

Pushkarna, M., Zaldivar, A., & Kjartansson, O. (2022). Data cards: Purposeful and transparent dataset documentation for responsible AI. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1776-1826). Association for Computing Machinery. https://doi.org/10.1145/3531146.3533231

Schmuck, D., Matthes, J., & Naderer, B. (2018). Misleading consumers with green advertising? An affect-reason-involvement account of greenwashing effects in environmental advertising. Journal of Advertising, 47(2), 127-145. https://doi.org/10.1080/00913367.2018.1452652

Schneider, M., Schelte, T., Schmitz, F., & Körner, M. (2023). EuroCrops: The largest harmonized open crop dataset across the European Union. Scientific Data, 10(1), Article 612. https://doi.org/10.1038/s41597-023-02517-0

Seele, P., & Gatti, L. (2017). Greenwashing revisited: In search of a typology and accusation-based definition incorporating legitimacy strategies. Business Strategy and the Environment, 26(2), 239-252. https://doi.org/10.1002/bse.1912

Spiegel, S. J. (2020). Visual storytelling and socioenvironmental change: Images, photographic encounters, and knowledge construction in resource frontiers. Annals of the American Association of Geographers, 110(1), 120-144. https://doi.org/10.1080/24694452.2019.1613953

Stephenson, J. (2008). The cultural values model: An integrated approach to values in landscapes. Landscape and Urban Planning, 84(2), 127-139. https://doi.org/10.1016/j.landurbplan.2007.07.003

Subramonyam, H., Seifert, C. M., & Adar, E. (2021). Towards a process model for co-creating AI experiences. In Proceedings of the 2021 ACM Designing Interactive Systems Conference (pp. 1529-1543). Association for Computing Machinery. https://doi.org/10.1145/3461778.3462012

Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Medical Imaging, 15(1), Article 29. https://doi.org/10.1186/s12880-015-0068-x

Taravat, A., Wagner, M. P., Bonifacio, R., & Petit, D. (2021). Advanced fully convolutional networks for agricultural field boundary detection. Remote Sensing, 13(4), Article 722. https://doi.org/10.3390/rs13040722

Tveit, M., Ode, Å., & Fry, G. (2006). Key concepts in a framework for analysing visual landscape character. Landscape Research, 31(3), 229-255. https://doi.org/10.1080/01426390600783269

Wagner, M. P., & Oppelt, N. (2020). Deep learning and adaptive graph-based growing contours for agricultural field extraction. Remote Sensing, 12(12), Article 1990. https://doi.org/10.3390/rs12121990

Waldner, F., & Diakogiannis, F. I. (2020). Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network. Remote Sensing of Environment, 245, Article 111741. https://doi.org/10.1016/j.rse.2020.111741

Waldner, F., Diakogiannis, F. I., Batchelor, K., Ciccotosto-Camp, M., Cooper-Williams, E., Herrmann, C., Mata, G., & Toovey, A. (2021). Detect, consolidate, delineate: Scalable mapping of field boundaries using satellite images. Remote Sensing, 13(11), Article 2197. https://doi.org/10.3390/rs13112197

Wu, T., Terry, M., & Cai, C. J. (2022). AI chains: Transparent and controllable human-AI interaction by chaining large language model prompts. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (Article 385, pp. 1-22). Association for Computing Machinery. https://doi.org/10.1145/3491102.3517582

Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020). Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-13). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376301

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Published

2025-05-30

How to Cite

From Field Geometry to Jewelry Interfaces: Multimodal AI-Assisted Shape Translation and Evidence-Grounded Agricultural Storytelling. (2025). International Journal of Graphic Design, 3(1), 218-240. https://doi.org/10.51903/ijgd.v3i1.4276