Harmonizing Urbanization and Food Security: A Novel AI-Driven Approach for Sustainable Urban Agriculture

Divyam Jindal

Abstract: The relentless wave of urbanization has given rise to a pivotal challenge: how to sustainably meet the escalating demand for food in the midst of rapidly expanding urban landscapes. In response, this research paper introduces an innovative AI-based model that leverages the power of satellite imaging, machine learning, and demographic projections to revolutionize urban agriculture. The model integrates soil analysis, water availability prediction, crop recommendations, and population growth projections into a cohesive framework for informed decision-making. By synergistically amalgamating these facets, the model provides a comprehensive toolkit that not only optimizes land use but also addresses the intricate balance between urbanization, food security, and environmental sustainability. Through this interdisciplinary approach, the paper presents a roadmap towards the harmonious coexistence of cities and agriculture, redefining urban spaces as hubs of both consumption and production.

Keywords: Urban Agriculture, Satellite Imaging, AI-based Model, Sustainability, Population Growth, Machine Learning, Soil Analysis, Water Availability Prediction, Crop Recommendations, Demographic Projections