The Impact of Data Analytics in Digital Agriculture: A Review

The advanced development in Information and Communication Technologies (ICT) and its adoption in the agriculture area open the field to the appearance of Digital Agriculture; which created new processes for making farming more productive, efficient, controllable while respecting the environment. Data science (and machine learning) is among key of information technology used in Digital Agriculture for their ability to analyse a vast amount of data to extract new knowledge and to help agriculture understand better the farming tasks and make better decisions. Big data in its turn offers a support to farmers to extract new insights from their data and to make more accurate decision. This work presents a systematic review of methods and techniques of (data & big data) mining and their applications to Digital Agriculture from the big data view point. In this study, we will focus on crop yield. We first introduce the crop yield management process and its components, and then we focus on the crop yield monitoring. We then present a classification of data mining techniques applied for the crop yield monitoring tasks. This is followed by discussing each category of the classification throughout a panoply of existing works and show their used techniques, then we provided a general discussion on the applicability of big data analytics into the field of digital agriculture.