Agricultural big data and methods and models for food security analysis-a mini-review

• Published in: PeerJ (San Francisco, CA) Vol. 10; p. e13674
• Main Authors: Ammar, Khalil A, Kheir, Ahmed M S, Manikas, Ioannis
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 29.06.2022; PeerJ, Inc; PeerJ Inc
• Subjects: Agricultural industry; Agricultural production; Agricultural Science; Agricultural societies; Agriculture; Bayes Theorem; Bayesian analysis; Big Data; Challenges; Data analysis; Data collection; Data entry; Data extraction; Data infrastructure; Data Mining and Machine Learning; Data Science; Datasets; Deep learning; Food Security; Food supply; Gap analysis; Gaps; Households; Humans; Information management; Innovations; International economic relations; Internet of Things; Investment analysis; Literature reviews; Mathematical models; Multi-model approach; Natural resources; Neural networks; Nutrition; Online databases; Population growth; Remote sensing; Research Design; Reviews; Safety and security measures; Sentiment analysis; Statistics; Text categorization; United Arab Emirates; China; United Arab Emirates; Data infrastructure; Multi-model approach; Visualization; Analysis; Challenges; United Arab Emirates; Gaps; Data extraction
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.13674

Big data and data analysis methods and models are important tools in food security (FS) studies for gap analysis and preparation of appropriate analytical frameworks. These innovations necessitate the development of novel methods for collecting, storing, processing, and extracting data. The primary goal of this study was to conduct a critical review of agricultural big data and methods and models used for FS studies published in peer-reviewed journals since 2010. Approximately 130 articles were selected for full content review after the pre-screening process. There are different sources of data collection, including but not limited to online databases, the internet, omics, Internet of Things, social media, survey rounds, remote sensing, and the Food and Agriculture Organization Corporate Statistical Database. The collected data require analysis ( , mining, neural networks, Bayesian networks, and other ML algorithms) before data visualization using Python, R, Circos, Gephi, Tableau, or Cytoscape. Approximately 122 models, all of which were used in FS studies worldwide, were selected from 130 articles. However, most of these models addressed only one or two dimensions of FS ( , availability and access) and ignored the other dimensions ( , stability and utilization), creating a gap in the global context. There are certain FS gaps both worldwide and in the United Arab Emirates that need to be addressed by scientists and policymakers. Following the identification of the drivers, policies, and indicators, the findings of this review could be used to develop an appropriate analytical framework for FS and nutrition.