ABSTRACT
Analyzing large databases for hidden connections, correlations and insights is known as big data analytics. Although many countries still use outdated farming methods, technological advancements have allowed for specific improvements (especially in developing countries). Big data analytics has the potential to expand the agricultural sector in this regard significantly. The farmers rely heavily on old methods for deciding what to plant and how to cultivate it. Walking through fields, selecting soil samples for moisture analysis, and visually inspecting plant leaves are typical examples of these time- honored practices. Understanding the significance of technology for acquiring crop information in considerable amounts and turning that data into usable knowledge is crucial for agriculturists (mainly farmers). Integration of big data could help agriculture make changes to its current practices. If used correctly, big data analytics can shed light on the most efficient crop cultivation methods. Extensive developments in three areas—crop prediction, precision farming and seed production—are reshaping the agricultural industry. There are four parts to this chapter. The first part of this paper provides an introduction to analytics on big data in agriculture. The second part will then focus on 106the various big data methods used in the agricultural sector. The third section provides two examples of how big data analysis methods were put to use in the field of agriculture. In the fourth section, the authors examine the several agricultural research avenues open to scholars and scientists. This chapter concludes with a brief overview.
