A neuron is a unit making decision on the basis of synapse signal (input 0-1) and a weight (importance) of this signal. Say we have a neuron whose synapse is coming from a soil moisture sensor. Weight (excitation threshold) is the level of the signal by which a neuron knows that the soil is dry enough and needs watering. But every plant has its own moisture level. That's where a backpropagation can be implemented.
By manually watering the flower pot and pressing the LEARN button we instruct our neuron what should be the weight of the signal or excitation threshold. The neurons are formed in Arduino UNO. Once the LEARN button is pressed, the average value of the moisture is recorded. When the soil becomes drier than the recorded value, the pump will be started by the relay.
Irrigation system based on AI by KARTHIKEYAN THIRUMALAISAMY is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License