Solar- and IoT-Based Smart Irrigation System using Fuzzy Logic for Sustainable Agriculture

Increasing the agriculture sector's growth is one of the best ways to guarantee both social stability and economic success. The economy in South Africa depends significantly on agriculture, which raises GDP and creates jobs. Given the current worldwide water crisis, an efficient and appropriate supply of irrigation water greatly increases agricultural productivity and promotes water conservation. The two primary water sources for agriculture are irrigation and rainfall. But the precipitation is not enough to meet the plant's water requirements. Conversely, too much moisture restricts plants' ability to absorb nutrients and raises the chance of disease development. This paper presents a predictive model that combines data on the state of the tank water level, dynamic soil moisture pattern of the field, and real-time weather forecast data (such as air temperature, humidity, and dewpoint) to help make more informed decisions about when and how much to irrigate. It is acknowledged that temperature and humidity are the primary factors in precipitation prediction, thus the Fuzzy Logic Controller receives these parameters, together with soil moisture, and computes them to yield the Field pump status. The information on the level of tank water, soil moisture level, air temperature, humidity, dewpoint, rainfall probability, tank pump and field pump are all displayed in the Blynk app. Renewable energy reduces the problem of greenhouse effect, which is a great problem to the planet, thus, the system utilizes solar combined with a battery storage as power supply. The system is described in depth in this paper, along with the results. Furthermore, the system operates flawlessly, and the forecasted outcomes are highly positive.