Design a Digital Twin Sensors System Using cGAN-LSTM for Greenhouse Climate Control

A route to sustainable greenhouse farming is provided by digital agriculture, notably through precision agriculture and Agriculture 4.0, to meet the expanding world population's food needs. This proposal aims to design a unique method to improve greenhouse climate sensors' data availability and dependability. We create a digital twin (DT) system by combining a predictive Conditional Generative Adversarial Network (cGAN) with Long Short-Term Memory (LSTM) networks. Our model uses time series data to forecast temperature, for example, by conditioning on variables such as time of day, month, and year, as well as environmental parameters like humidity and CO2 levels. These forecasts make it possible to identify mistakes and faults in sensors, which enhances the precision of temperature management and may save sensor costs.