Abstract
This work in progress delves into the intricate intersection of sustainable agriculture, emphasizing the transformative potential of Big Data and Artificial Intelligence (AI) in mitigating global food challenges. As the world confronts climate change, population growth, and the repercussions of unsustainable farming, the urgency for innovative solutions intensifies. Navigating through the historical trajectory of agricultural advancements, the narrative underscores the necessity for a paradigm shift toward sustainable practices. Big Data and AI emerge as pivotal catalysts, bridging gaps between individual and global trends, and providing data-driven insights to address the multifaceted challenges in global agriculture. The literature review elucidates the divergence in technology adoption between large-scale and small-scale farming, identifying economic and infrastructural barriers impeding progress. Environmental consequences of traditional farming practices take precedence, advocating for a transition to agroecological approaches. The paper’s focus lies in exploring the accessibility of technological advancements to small-scale farmers, historically marginalized in industrial agricultural developments. Research questions delve into the accessibility of AI and Big Data for small-scale farmers and their potential to enhance access to agroecological technology. The methodology outlines an approach, incorporating a review of existing data, analysis of publicly available datasets, and case studies in specific regions. Expected results include insights into technology adoption barriers and recommendations for improved access. This work signifies a crucial contribution to the ongoing discourse on sustainable agriculture and technology.
Presenters
Monica DantasStudent, PhD in Social Science - Interdisciplinary, Charlotte County Public School, Florida, United States
Details
Presentation Type
Theme
KEYWORDS
Sustainability, Technology Adoption, Artificial Intelligence, Big Data, Small_scale farming
