Digital twin enhanced with Machine Learning Algorithms for Irrigation Management Using Sensor Data

Efficient irrigation management is critical for maximizing agricultural output while minimizing water waste. This study investigates the effectiveness of machine learning (ML) for data-driven irrigation decision support systems (DSS) to address the scalability issue in artificial intelligence applications. Therefore, the scientific literature highlights how scalability is the main issue that needs to be investigated. In this framework, a living lab focused on an olive cultivation in a greenhouse has been developed to train and test a digital twin application enhanced with machine learning algorithms. Specifically, the algorithm classifies the irrigation status by assessing soil and environmental sensor data. The integration between the physical and the virtual counterparts has been implemented exploiting a Message Queuing Telemetry Transport (MQTT) communication protocol, where soil and environmental data have been treated as input topics. The model performance has been evaluated by calculating accuracy, precision, recall, F1-score, and confusion matrices, which highlight the effectiveness of the model in managing irrigation by assessing no-crop specific features with the aim of improving the scalability in greenhouse applications.