Climatic Pattern Analysis Using Neural Networks in Smart Agriculture to Maximize Irrigation Efficiency in Grass Crops in Rural Areas of Ecuador

This project describes the development of a system that allows to improve the efficiency of a sprinkler irrigation system based on sensor networks and artificial neural networks. The WSN network is composed of a sensor node, a central node, and an actuator node. The sensor node consists of soil moisture, UV radiation, temperature, and rain sensors. The data collected by the sensor node is sent to the central node or Gateway through LoRa communication where the neural network is trained. The neural network makes predictions of irrigation status it predicts when the crop needs irrigation. Subsequently, the actuator node executes order sent from central node; activates or deactivates the solenoid valve allowing or denying the water flow to the sprinklers. A comparison between the manual irrigation system and the automated system with the neural network, where it is verified that automated system reduces water consumption and contributes to the development grass crop.