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

SCC-IRG 第 2 轨：侨民、农业、

• 批准号: 2310515
• 负责人: Sucheta Ghoshal
• 金额: $ 166万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-12-15 至 2026-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310515&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Diaspora; Agriculture

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.

黑人流散农业社区是美国和全球数百万人的可持续粮食生产的重要场所。作为紧密联系的农业集体，它们在整个城市中产生食物，促进生态福祉，资源保护和相互依存关系的流散价值，并促进黑人，个人，种族主义和边缘化群体的社会和政治转型的可能性。尽管粮食安全，营养和环境健康方面的进步有可能每年养活几乎两倍的人，尤其是在历史上边缘化的群体中，但黑人流散城市农业社区面临着几个障碍，达到了效率的几个障碍，这些障碍在很大程度上仍然不足。风险管理，土壤健康监测和收获等活动取决于反复的，耗时的工作，这些工作挑战了预算有限，资源和劳动力。人工智能领域（AI）有望解决许多此类挑战的解决方案，这为AI驱动的农业技术围绕新兴市场提供了理由。该项目的目的是（1）提前了解涉及AI的社会技术生态系统，以支持流离失所的城市农业； （2）共同开发基于AI的技术，以更好地整合和维持侨民知识的技术收益，（3）系统地评估基于AI的农业技术对侨民社区和工业伙伴的影响。特别是，我们的研究旨在推进沿着三个紧急斧头的流离失所城市农业社区的智能农业领域：（1）劳动：满足劳动需求，减少除草中的偏见，并确保为需要它们的农民提供负担得起的服务； （2）生态系统：通过支持与土地和周围组织的协同关系，培训新颖的农民和监测温室状况来帮助对农场的生态状况的护理； （3）健康：针对健康土壤条件的创新机制，降低毒性以及增加养分的质量和数量。这项工作在三个阶段展开。第1阶段始于人种学案例研究，涉及调查城市农业实践的参与者以及与合作伙伴组织和确定利益相关者的半结构化访谈。第2阶段通过一项评估和设计研究完成了这项经验工作，该研究确定了社会技术解决方案，以通过黑人流散需求告知农业决策。第三阶段涉及技术实施，将黑人流散知识与基于AI的技术集成到智能和连接的流放性农业基础设施中。这项工作依赖于我们跨学科团队与三个农业组织，三个行业合作伙伴以及在广泛散居的农业网络之间持续的合作伙伴关系的密切合作，以及对AI，HCI，批判地理，城市研究和社区询问的专家的监督，这些项目与不断的研究范围进行了旨在筹集的研究（AIS基于社区的练习），该项目的旨在融合了（A STEM学习），该计划的努力（A STEM学习）是一定的，该计划的努力（A STEM学习）是一项旨在的（A STEM），这是一项旨在的计划，该计划是一项旨在的计划，该计划是一项计划，这是一项旨在的计划，这是一项旨在的计划，这是一项旨在的计划。 STEM学习（ISL）的经验和环境使终身学习成为现实。该奖项反映了NSF的法定使命，并且我们使用基金会的知识分子优点和更广泛的影响审查标准，通过评估诚实地表示支持。