SCC-IRG Track 2: Diaspora, Agriculture, & AI: Community-based Integration of Smart Technologies into Black Diasporic Agricultural Practices

黑人散居的农业社区是美国和世界各地数百万人可持续粮食生产的重要场所。作为紧密团结的农业集体，他们在整个城市生产粮食，促进生态福祉、资源保护和相互依存的散居价值观，并为黑人、土著、种族化和边缘化群体创造社会和政治变革的可能性。虽然粮食安全、营养和环境健康方面的进步有可能每年养活近两倍的人口，特别是在历史上被边缘化的群体中，但黑人散居的城市农业社区面临着一些效率障碍，这些障碍在很大程度上仍未得到解决。风险管理、土壤健康监测和作物收割等活动依赖于重复、耗时的工作，而这些工作在有限的预算、资源和劳动力的支持下具有挑战性。人工智能（AI）领域有望解决其中许多挑战，为围绕AI驱动的农业技术的新兴市场提供了理由。该项目旨在（1）促进对涉及人工智能的社会技术生态系统的理解，以支持散居的城市农业;（2）合作开发基于人工智能的技术，更好地将技术收益与散居知识相结合并维持技术收益，以及（3）系统地评估基于人工智能的农业技术对散居社区和工业合作伙伴的影响。特别是，我们的研究旨在推动智能农业领域的散居城市农业社区沿着三个紧迫的轴：（1）劳动力：解决劳动力需求，减少除草中的偏见，并确保需要他们的农民获得负担得起的服务;（2）生态系统：通过支持与土地和周围生物的协同关系，促进对农场生态条件的照顾，培训新手农民，监测温室条件;（3）健康：创新土壤健康机制，减少毒性，提高养分的质量和数量。这项工作分三个阶段展开。第一阶段从人种学案例研究开始，涉及参与者对城市农业实践的观察以及与合作伙伴组织和确定的利益攸关方的半结构化访谈。第二阶段通过评估和设计研究补充了这一经验性工作，该研究确定了根据黑人散居地需求进行农业决策的社会技术解决方案。第三阶段涉及技术实施，将黑人散居知识与基于人工智能的技术相结合，以实现智能和互联的散居农业基础设施。这项工作依赖于我们的跨学科团队与三个农业组织，三个行业合作伙伴的密切合作，以及更广泛的散居农业网络的持续合作伙伴关系，以及人工智能，HCI，批判性地理，城市研究和基于社区的调查专家的监督。该项目部分由推进非正式STEM学习（AISL）计划资助，致力于资助研究和实践，并继续关注一系列非正式STEM学习（ISL）该奖项反映了NSF的法定使命，并被认为是值得通过评估使用的支持基金会的学术价值和更广泛的影响审查标准。

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.