The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data
Accurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwester...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3658 |
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| author | Simona Stojanova Mojca Volk Gregor Balkovec Andrej Kos Emilija Stojmenova Duh |
| author_facet | Simona Stojanova Mojca Volk Gregor Balkovec Andrej Kos Emilija Stojmenova Duh |
| author_sort | Simona Stojanova |
| collection | DOAJ |
| description | Accurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwestern Idaho, the United States of America (USA), over a period of three years (2017–2019). Focusing on long-term irrigation forecasting, addressing a critical gap in sustainable water management, we use machine learning (ML) methods to predict future irrigation needs, with improved accuracy. We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. We validate the results of the LSTM model using a cross-validation technique, wherein a mean MSE of 0.18 was achieved. The low value of the statistical analysis (<i>p</i>-value = 0.0009) of a paired <i>t</i>-test confirmed that the improvement is significant. This research shows the potential of Artificial Intelligence (AI) to optimize irrigation planning and advance sustainable precision agriculture (PA), by providing a practical tool for long-term forecasting and that supports data-driven decisions. |
| format | Article |
| id | doaj-art-069030fcdafd4d349d1b7ec5f97d0960 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-069030fcdafd4d349d1b7ec5f97d09602025-08-20T02:21:47ZengMDPI AGSensors1424-82202025-06-012512365810.3390/s25123658The Future of Vineyard Irrigation: AI-Driven Insights from IoT DataSimona Stojanova0Mojca Volk1Gregor Balkovec2Andrej Kos3Emilija Stojmenova Duh4Faculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Tržaška cesta 25, 1000 Ljubljana, SloveniaAccurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwestern Idaho, the United States of America (USA), over a period of three years (2017–2019). Focusing on long-term irrigation forecasting, addressing a critical gap in sustainable water management, we use machine learning (ML) methods to predict future irrigation needs, with improved accuracy. We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. We validate the results of the LSTM model using a cross-validation technique, wherein a mean MSE of 0.18 was achieved. The low value of the statistical analysis (<i>p</i>-value = 0.0009) of a paired <i>t</i>-test confirmed that the improvement is significant. This research shows the potential of Artificial Intelligence (AI) to optimize irrigation planning and advance sustainable precision agriculture (PA), by providing a practical tool for long-term forecasting and that supports data-driven decisions.https://www.mdpi.com/1424-8220/25/12/3658sustainable agricultureirrigation predictioninternet of thingssensorslinear regressionLSTM |
| spellingShingle | Simona Stojanova Mojca Volk Gregor Balkovec Andrej Kos Emilija Stojmenova Duh The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data Sensors sustainable agriculture irrigation prediction internet of things sensors linear regression LSTM |
| title | The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data |
| title_full | The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data |
| title_fullStr | The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data |
| title_full_unstemmed | The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data |
| title_short | The Future of Vineyard Irrigation: AI-Driven Insights from IoT Data |
| title_sort | future of vineyard irrigation ai driven insights from iot data |
| topic | sustainable agriculture irrigation prediction internet of things sensors linear regression LSTM |
| url | https://www.mdpi.com/1424-8220/25/12/3658 |
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