Black diasporic farming communities are important sites of sustainable food production for millions of people in the US and worldwide. As tight-knit agricultural collectives, they generate food throughout cities, promote diasporic values of ecological well-being, resource conservation, and interdependence, and foster the possibility of social and political transformation for black, indigenous, racialized and marginalized groups. While advancements in food security, nutrition, and environmental health have the potential to feed nearly twice as many people per year, particularly among historically marginalized groups, black diasporic urban farming communities face several hurdles to efficiency that remain largely under-addressed. Activities such as risk management, soil health monitoring, and crop harvesting rely on repeated, time-consuming work that is challenging to support on limited budgets, resources, and labor. The field of artificial intelligence (AI) promises solutions to many of these challenges, making the case for an emerging market around AI-driven agricultural technologies. This project aims to (1) advance understanding of sociotechnical ecosystems involving AI to support diasporic urban farming; (2) collaboratively develop AI-based technologies that better integrates and sustains technological gains with diasporic knowledge, and (3) systematically assess the impact of AI-based farming technologies on diasporic communities and industrial partners.

In particular, our research seeks to advance the field of smart agriculture for diasporic urban farming communities along three urgent axes: (1) Labor: Addressing labor needs, decreasing bias within weeding, and ensuring access to affordable services for farmers who need them; (2) Ecosystem: Advancing care for a farm’s ecological conditions by supporting synergistic relationships with the land and surrounding organisms, training novice farmers, and monitoring greenhouse conditions; (3) Health: Innovating mechanisms for healthy soil conditions, reducing toxicity, and increasing the quality and quantity of nutrients. This work unfolds across three phases. Phase 1 begins with an ethnographic case study involving participant observation of urban agricultural practices and semi-structured interviews with partner organizations and identified stakeholders. Phase 2 complements this empirical work with an evaluation and design study that identifies sociotechnical solutions for agricultural decision-making informed by black diasporic needs. Phase 3 involves technical implementation that mindfully integrates black diasporic knowledge with AI-based technologies towards a smart and connected diasporic farming infrastructure. This work relies on our interdisciplinary team’s close collaboration with three farming organizations, three industry partners, and sustained partnerships across wider diasporic farming networks, and oversight from experts in AI, HCI, critical geography, urban studies, and community-based inquiry.

This project is partially funded by the Advancing Informal STEM Learning (AISL) program, which is committed to funding research and practice with continued focus on investigating a range of informal STEM learning (ISL) experiences and environments that make lifelong learning a reality.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.