A Survey on Data Science for Food Security in Developing Regions

Abstract

In developing regions, food security remains a pressing issue influenced by various socioeconomic, climatic, and infrastructural factors. This research paper evaluates the way data science can address these challenges by improving agricultural productivity, optimizing resource allocation, and predicting food shortage by leveraging forecasting analytics, artificial intelligence, and geospatial data, this research paper point out the pioneering methods to enhance food security. Challenges like data accessibility and technological disparities are also discussed. The findings aim to guide policymakers and stakeholders in leveraging data-driven solutions to ensure sustainable food systems in these regions.

Data science is a new approach that uses advanced technologies such as machine learning, geospatial analysis, and predictive modeling to solve long-standing agricultural challenges. These approaches are essential in reducing waste, increasing yield, and enhancing the adaptability of food systems to climate variability. The integration of these tools aligns with global efforts to achieve sustainability, making data science a cornerstone in transforming agricultural practices for food security.