AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study
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 t...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10988780/ |
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| author | Mihaela Hnatiuc Domnica Alpetri Sorin-Robertino Sintea Bogdan Hnatiuc Gabriel Margarit Raicu Mirel Paun Ionica Dina |
| author_facet | Mihaela Hnatiuc Domnica Alpetri Sorin-Robertino Sintea Bogdan Hnatiuc Gabriel Margarit Raicu Mirel Paun Ionica Dina |
| author_sort | Mihaela Hnatiuc |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9bcdc2ef2a7840229bb869a3b78e5029 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9bcdc2ef2a7840229bb869a3b78e50292025-08-20T03:49:22ZengIEEEIEEE Access2169-35362025-01-0113802588027110.1109/ACCESS.2025.356745410988780AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete StudyMihaela Hnatiuc0https://orcid.org/0000-0002-0410-0530Domnica Alpetri1Sorin-Robertino Sintea2Bogdan Hnatiuc3Gabriel Margarit Raicu4Mirel Paun5https://orcid.org/0000-0003-0851-8296Ionica Dina6Electronics and Telecommunications Department, Constanţa Maritime University, Constanta, RomaniaDoctoral School of Electronics, Telecommunications and Information Technology, National University of Science and Technology POLITEHNICA Bucharest, Bucharest, RomaniaElectrical Engineering Department, Constanţa Maritime University, Constanta, RomaniaElectrical Engineering Department, Constanţa Maritime University, Constanta, RomaniaDepartment of Navigation, Constanţa Maritime University, Constanta, RomaniaElectronics and Telecommunications Department, Constanţa Maritime University, Constanta, RomaniaResearch Station for Viticulture and Oenology Murfatlar, Murfatlar, RomaniaThis 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.https://ieeexplore.ieee.org/document/10988780/Artificial intelligence of Internet of Thingscloud computingclusterIoT application programming interface (API)smart environment |
| spellingShingle | Mihaela Hnatiuc Domnica Alpetri Sorin-Robertino Sintea Bogdan Hnatiuc Gabriel Margarit Raicu Mirel Paun Ionica Dina AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study IEEE Access Artificial intelligence of Internet of Things cloud computing cluster IoT application programming interface (API) smart environment |
| title | AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study |
| title_full | AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study |
| title_fullStr | AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study |
| title_full_unstemmed | AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study |
| title_short | AIoT Monitoring for Early Identification of Diseases in Grapevines: Complete Study |
| title_sort | aiot monitoring for early identification of diseases in grapevines complete study |
| topic | Artificial intelligence of Internet of Things cloud computing cluster IoT application programming interface (API) smart environment |
| url | https://ieeexplore.ieee.org/document/10988780/ |
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