SMART IRRIGATION CONTROLLER USING SOIL AND WEATHER ANALYTICS

The Smart Irrigation Controller Using Soil and Weather Analytics is an IoT project that is aimed at managing irrigation and reducing water wastage in agriculture. This project incorporates an ESP32 microcontroller to sense moisture in the soil, soil temperature, humidity, and availability of water by using sensors. The soil moisture sensor can be used to determine if there is a need for watering the soil. The DHT11 sensor is used to sense the temperature and humidity of the surroundings. Water level sensor is used to determine the presence of water in the storage tank and protects the pump from being run without enough water. According to the data collected by the sensors, the ESP32 automatically determines the decision for irrigation and operates the pump with the help of the relay module. If the soil is dry and there is enough water, the pump is turned ON. Otherwise, it is turned OFF. With this automation process, the requirement of constant human presence becomes less and unnecessary pumping of water can be avoided. Unlike traditional irrigation processes which use manual methods or fixed-time irrigation schemes, the presented method takes into consideration the soil moisture levels and water availability while controlling the irrigation process. The design has been made with the use of commonly available electronic parts and hence is appropriate for small farms, personal gardens, greenhouses, and nurseries. The design serves as a good learning tool regarding embedded systems, sensor interfacing, IoT applications, and automated control processes in agriculture. Temperature and humidity measurement have been used to add environmental parameters that will be considered for future improvement in irrigation process. However, at present the system is mostly dependent on sensor readings and control conditions.  So more development needs to be done for better decision making related to irrigation according to weather conditions and requirements of different crops. In future, real-time weather forecasting can be incorporated by using weather API.  Machine learning methods can be employed to determine irrigation needs, while solar energy will serve as an alternate source of power for farms. Notification services via mobile apps can also be incorporated into the system to alert users about the working of the pump and low water level. Further tests will have to be conducted on various soil types, environmental conditions, and irrigation needs to determine the reliability of the system, its water consumption, and its energy efficiency. As the experimental results have not yet been obtained, the exact amount of water and energy saved will have to be established through practical tests. In general, the suggested project presents an economical way to automate the irrigation process and leaves room for improvements.  

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