A comprehensive review of Data Mining techniques in smart agriculture

Abstract

Agriculture remains a vital sector for most countries. It presents the main source of food for the population of the world. However, it faces a big challenge: producing more and better while increasing the sustainability with a reasonable use of natural resources, reducing environmental degradation as well as adapting to climate change. Hence, it is extremely important to switch from traditional agricultural methods to modern agriculture. Smart Agriculture is one of the solutions to deal with the growing demand for food while meeting sustainability requirements. In Smart Agriculture, the role of information is increasing. Information on weather conditions, soils, diseases, insects, seeds, fertilizers, etc. constitutes an important contribution to the economic and sustainable development of this sector. Smart management consists of collecting, transmitting, selecting and analyzing data. As the amount of agricultural data increases significantly, robust analytical techniques capable of processing and analyzing large amounts of data to obtain more reliable information and much more accurate predictions are essential. Data Mining is expected to play an important role in Smart Agriculture for managing real-time data analysis with massive data. The aim of this paper is to review ongoing studies and research on smart agriculture using the recent practice of Data Mining, to solve a variety of agricultural problems.