  Irrigation decision systems and water need models have been important
research topics in agriculture since 90s. They improve the efficiency of crop
yields, provide an appropriate use of water on the earth and so, prevent the
water scarcity in some regions. In this paper, a comprehensive survey on water
need models depending on crop growth and irrigation decision systems has been
conducted based on machine learning and advanced control theory. The following
outcomes and solutions are the main contributions. First, crop growth models
and correspondingly water need models are suffer from un-modeled dynamics of
the environment and lack of sensory devices. Second, irrigation decision
systems based on the controller design are not fully efficient due to the
imprecise crop growth models and time-varying environments. Third, water need
models are depending on the inaccurate weather forecasts that also causes
inefficient irrigation control. The relevant literature basis to these outcomes
are surveyed in detail. Then, available and foreseen solutions are discussed
and presented on a time-line. Consequently, literature review with the latest
developments on water need models, irrigation decision systems, applied control
methods and discussions are expected to be useful for the future strategies.
