  In general. automated farming systems make decisions based on static models
built from the properties of the plant. in the contrast, irrigation decisions
in our suggested method are dynamically changing environmental conditions. the
model"s learning process reveals the mathematical links between the
environmental factors employed in the determining the irrigation habit and
gradually improves its learning techniques as irrigation data accumulates int
the model. to analyze overall system overall system performance, we constructed
a test environment for the sensor edge, mobile client, and decision service in
the cloud.
