IoT-Driven Machine Learning for Precision Viticulture Optimization
Precision agriculture (PA), also known as smart farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, precision viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (I...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10368294/ |
| _version_ | 1827126029079019520 |
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| author | Chiara Pero Sambit Bakshi Michele Nappi Genoveffa Tortora |
| author_facet | Chiara Pero Sambit Bakshi Michele Nappi Genoveffa Tortora |
| author_sort | Chiara Pero |
| collection | DOAJ |
| description | Precision agriculture (PA), also known as smart farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, precision viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher resolution meteorological and soil data obtained through in situ sensing. The integration of machine learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. These data allow ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a real-world scenario involving a vineyard located in Southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices. |
| first_indexed | 2024-03-08T15:35:20Z |
| format | Article |
| id | doaj.art-276e80d9f2e645c3bcc5294ec8e33c2f |
| institution | Directory Open Access Journal |
| issn | 1939-1404 2151-1535 |
| language | English |
| last_indexed | 2025-03-20T15:06:06Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-276e80d9f2e645c3bcc5294ec8e33c2f2024-09-05T23:00:35ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01172437244710.1109/JSTARS.2023.334547310368294IoT-Driven Machine Learning for Precision Viticulture OptimizationChiara Pero0https://orcid.org/0000-0002-5517-2198Sambit Bakshi1https://orcid.org/0000-0002-6107-114XMichele Nappi2https://orcid.org/0000-0002-2517-2867Genoveffa Tortora3https://orcid.org/0000-0003-4765-8371Department of Management & Innovation Systems, University of Salerno, Fisciano, ItalyDepartment of Computer Science and Engineering, National Institute of Technology, Rourkela, IndiaDepartment of Computer Science, University of Salerno, Fisciano, ItalyDepartment of Computer Science, University of Salerno, Fisciano, ItalyPrecision agriculture (PA), also known as smart farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, precision viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher resolution meteorological and soil data obtained through in situ sensing. The integration of machine learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. These data allow ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a real-world scenario involving a vineyard located in Southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.https://ieeexplore.ieee.org/document/10368294/Artificial intelligence (AI)frostgrapevine diseasesInternet of Things (IoT)precision agriculture (PA)precision viticulture (PV) |
| spellingShingle | Chiara Pero Sambit Bakshi Michele Nappi Genoveffa Tortora IoT-Driven Machine Learning for Precision Viticulture Optimization IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Artificial intelligence (AI) frost grapevine diseases Internet of Things (IoT) precision agriculture (PA) precision viticulture (PV) |
| title | IoT-Driven Machine Learning for Precision Viticulture Optimization |
| title_full | IoT-Driven Machine Learning for Precision Viticulture Optimization |
| title_fullStr | IoT-Driven Machine Learning for Precision Viticulture Optimization |
| title_full_unstemmed | IoT-Driven Machine Learning for Precision Viticulture Optimization |
| title_short | IoT-Driven Machine Learning for Precision Viticulture Optimization |
| title_sort | iot driven machine learning for precision viticulture optimization |
| topic | Artificial intelligence (AI) frost grapevine diseases Internet of Things (IoT) precision agriculture (PA) precision viticulture (PV) |
| url | https://ieeexplore.ieee.org/document/10368294/ |
| work_keys_str_mv | AT chiarapero iotdrivenmachinelearningforprecisionviticultureoptimization AT sambitbakshi iotdrivenmachinelearningforprecisionviticultureoptimization AT michelenappi iotdrivenmachinelearningforprecisionviticultureoptimization AT genoveffatortora iotdrivenmachinelearningforprecisionviticultureoptimization |