A comparative study of big data use in Egyptian agriculture

Abstract

Abstract The Egyptian economy relies heavily on the agricultural sector. As the population grows, arable land will diminish in the next decades. This makes food supply a priority. Big data could help the agriculture sector to address food security, especially in Egypt. In this paper, we examined the role of big data in agriculture in response to three questions: (1) What are the trend in peer-reviewed papers in the field of business development modeling and management? (2) What approaches were widely used especially in underdeveloped countries? (3) What is the current gap in terms of data sources, modeling, and analytic methods? As a result, 242 peer-reviewed articles have been studied. The contribution and findings of this study are summarized as. (1) A briefing on popular approaches which used frameworks was provided. (2) Publications based on the Internet of Things (IoT) in agriculture have increased dramatically by about 27%, 40%, and 44% in the years 2017, 2018, and 2019, respectively. (3) Around 37% of publications used Landsat and Sentinel-2 satellite images to build popular vegetation indices and land cover maps. (4) The challenges were identified as well as substantial opportunities that might serve as a roadmap for future growth. Therefore, by performing a comparative study in big data from this perspective, we explored the design principles using artificial intelligence and discussed a converged architecture to address the above-mentioned challenges.

Keywords

• Agriculture
• Big data
• Systematic literature review (SLR)
• Geographical information systems (GIS)
• Internet of Things (IoT)
• Machine learning (ML)