Digital twin technology is a virtual representation of an already existing physical model that can be used to replace the physical process or system. This paper proposes the development of a twin technology to fertigate vegetable crops in a controlled environment, using IoT and Deep Learning techniques, to ensure optimal use of water and fertilizers. Data collection is done using IoT sensors and pushed into the cloud-based computing engine. Data analytics is done to the time series data using ARIMA, SARIMA, Prophet and LSTM models. Based on the values for NPK, provided by the models is used for fertigation on future crops using automated IoT technology. This optimum use of water and fertilizers ensure that the crops get sufficient water and nutrition for its growth without compromising on the quality or quantity of harvest, without wastage of water and without changing the constitution of the soil.