Abstract
Big data and cloud computing technologies have become popular technologies for information processing across different sectors of the economies including agriculture. This chapter aims to examine the usage of big data and cloud computing in Sub-Saharan African agriculture. According to literature, agriculture in Africa generally faces production challenges and this is attributed to limitations in information access and processing, coupled with farmers' reluctance to adopt new farming practices and digital technologies. After a review of literature on the use of these technologies in Africa, the chapter proposed an integrated model for supporting agriculture through big data and cloud computing.
TopBackground
As indicated in Figure 1, the SSA region consists of 49 countries. Because of its vulnerability to natural and climate-related calamities, this region has attracted international attention. On the other hand, opportunities appearing in this region in the areas of natural resource exploitation and agricultural development have been positive attractions to this location.
Figure 1. Sub-Saharan countries (Source: Swiss Federal Department of Foreign Affairs, 2021)
Agriculture and mining are two of the most prominent economic activities in these SSA countries. The bulk of the population in these nations lives in rural regions and relies on agriculture either directly or indirectly (Organisation for Economic Co-operation and Development/Food and Agriculture Organisation, 2016). Despite that, reduced agricultural output in SSA has resulted in hunger for the majority of the people, making agricultural production and food security a serious concern for this region. Raidimi and Kabiti (2019) advised effective knowledge dissemination to farmers for enhanced decision making using big data and cloud computing as viable instruments for improved on-farm decision making in an assessment of agricultural extension systems in South Africa.
TopMain Focus Of The Chapter
The obstacles that SSA farmers experience in their farming activities are the topic of this chapter. The chapter also delves into big data and cloud computing principles and their applications in agriculture, big data and cloud computing models, and problems in adopting these technologies. Following that, a proposed paradigm for supporting on-farm decision making that integrates big data and cloud computing models is presented.
Key Terms in this Chapter
Smart Agriculture: The usage of digital tools to support agricultural activities.
Machine Learning: Computer capabilities to learn and adapt without following explicit user instructions.
Classification: Arrangement of objects into groups of items according to their observed similarities.
GIS: A system that creates and analyses maps and data.
Sub-Saharan Agriculture: Agriculture south of the Sahara.
Clustering: Finding groups of items that are similar to each other but are different from other objects.
Cloud Computing Model: An integration of technologies satisfying cloud computing applications in a domain.
Big Data Model: An integration of technologies satisfying big data applications in a domain.