AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study - Mihaela Hnatiuc; Domnica Alpetri; Sorin-Robertino Sintea; Bogdan Hnatiuc; Gabriel Margarit Raicu; Mirel Paun; Ionica Dina (IEEE Access)
This study explores the application of the Artificial Intelligence of Things (AIoT) in viticulture for the early detection of grapevine diseases. By integrating Internet of Things (IoT) sensors with machine learning algorithms, the system is designed to detect potential grapevine pathogens in real time. Deployed at the Murfatlar vineyard in Romania, which grows Cabernet Sauvignon and Sauvignon Blanc, the system allows for proactive disease management, thus improving grapevine health and reducing crop losses. IoT sensors are installed in the field to collect real-time data on grapevine health, which is then transmitted to the cloud for storage and analysis. Machine learning (ML) algorithms, running on a server with an NVIDIA R3900 card, process this data to predict potential infections caused by pathogens such as Plasmopara viticola, Uncinula necator, and Botrytis. Cloud computing facilitates data processing and real-time visualization, allowing farmers to make timely, data-driven decisions for disease control. The paper outlines the hardware and software components that constitute the diagnostic system.