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Designing Sustainable Food Cultures through Computational Technologies


Recent scholarships in sustainable interaction design have advocated “working with nature” as a potentially efficacious alternative to human efforts to control it: yet it is less clear how to do so. To understand how to support sustainable farming practices through technology, I conducted long-term ethnographic fieldwork with small-scale and environmentally friendly farmers in rural Taiwan and Indiana, USA. Common among these sites are the farmers’ commitment to the elimination of pesticides and herbicides and the cultivation of care-based symbiotic relationship with the land and nonhuman species. The findings offer insights to leverage computational technologies (e.g., computer vision, artificial intelligence) to support sustainable food cultures. 


  • Ethnography

  • Virtual ethnography

  • Contextual interview

  • Contextual inquiry

  • Participant observation

  • Cultural probe 

  • What-if cards

  • Close reading 


  • Support precision farming initiatives to minimize the use of noxious herbicide and pesticides while maximizing yields and profits. One potential application is to utilize computer vision and machine learning to enable selective weeding and reduce chemical spray.

  • Incentivize farmers to switch from traditional industrial farming (e.g., large-scale monoculture, heavy use of chemical compounds) to sustainable precision agriculture to better ensure food safety, cultivate biodiversity, reduce agricultural runoff, and mitigate environmental pollution.

  • Incubate innovative "AI for good" applications to support global sustainability; read about the United Nations Sustainable Development Goals here. 



Shaowen Bardzell (research manager), Jeffrey Bardzell (research manager), Patrycja Zdziarska (researcher), Pei-Ni Chiang (researcher), Xi Lu (researcher). 



*This project was funded by the National Science Foundation (US) and the Ministry of Education, Taiwan. 

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