Deep Learning and Internet of Things (IOT) Based Irrigation System for Cultivation of Paddy Crop

Abstract

Agriculture has a significant role in cultural life, and Agriculture has a significant role in the economies of many countries. To come up with the best possible conclusion from this study. It's essential to pay attention to critical factors, including energy, water availability, labour, and a correct watering plan for crops. Researchers in this study were interested in building a smartphone application that would allow farmers to operate an IoT-based automated irrigation system remotely. Paddy field photos were used to create a deep learning model called Paddy Field Radial Basis Function Networks (PF-RBFNs). The model tells the farmer how much water will be needed in a certain field area for irrigation. A real-time picture dataset and a raspberry pi-based hardware model were used to test this approach. The model was compared to three different deep learning models: LSTMs, RNNs, and GANs. It was found that this proposed PF-RBFNs model has a 93% accuracy rate.