Weather-Aware Smart Irrigation System Using Temporal Fusion Transformer for Precision Agriculture

In the world of variable weather & water scarcity, it is clear that water management is absolutely critical in agriculture. We present a water-aware smart irrigation system that makes use of a Temporal Fusion Transformer (TFT) for scheduling water based on weather forecasting, soil moisture, and historical trends. It integrates Internet of Things (IoT) sensors to collect up-to-minute data regarding soil moisture, temperature, humidity, and rain forecasts from trusted meteorological services. We preprocess the collected data and put it into the TFT model that performs multi-horizon time-series forecasting while also being able to utilize static and dynamic variables for interpretability. We validate our method using a real-world dataset from agricultural fields where crops are cultivated across varied climatic conditions. The experimental results show that the prediction accuracy and water efficiency of the proposed model are better than the traditional model such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) models. This conserves huge quantities of water and facilitates suitable moisture retention in soil for maximum crop yield. Furthermore, the feature importance analysis of the TFT improves interpretability in the decision made and helps farmers transition to data-driven irrigation. Therefore, the proposed study aims to promote sustainable agriculture by sustaining irrigation management based on deep learning and IoT technologies in an intelligent, scalable, and efficient manner.