A Review Based On Comparative Analysis of Techniques Used in Precision Agriculture

Agriculture is defined as a branch of science related to soil cultivation, crops growth and animals rearing for food supply and production of wool among other products. It plays pivotal role on the GDP (Gross Domestic Product) of any country. One way to increase the Agri-based GDP is to use Precision Agriculture (PA) whereby information technology is introduced into traditional agriculture. Agricultural expert systems have progressed from basic record keeping to large-scale farm management information systems (FMISs) that are utilized for crop prediction, crop disease detection, farm scheduling, and water monitoring. Precision Agriculture based framework allows us to use proper amount of water, fertilizers and seeds thereby, increasing the productivity of the agriculture fields by monitoring the environmental/soil parameters of agricultural land such as: pH, soil temperature, soil humidity, soil moisture and atmospheric pressure. In this paper an extensive literature review has been done on the architecture, hardware, communication protocol, and data acquisition infrastructure for crop monitoring systems along with the survey of different mobile applications and machine learning models used in precision agriculture.