20.500.12556/RUL-169929 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 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 (p-value = 0.0009) of a paired t-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. sustainable agriculture irrigation prediction internet of things sensors linear regression LSTM trajnostno kmetijstvo napoved namakanja internet stvari senzorji linearna regresija LSTM true false true Angleški jezik Slovenski jezik Članek v reviji 2025-06-18 10:04:39 2025-06-18 10:04:48 2025-06-19 03:56:37 0000-00-00 00:00:00 2025 0 0 19 str. issue 12, art. 3658 Vol. 25 Jun.-2 2025 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 004:634.8 1424-8220 10.3390/s25123658 239775491 sensors-25-03658-v2.pdf sensors-25-03658-v2.pdf 1 5FC05C7AE8303BE8E4F04C1120CE96D8 4cbeea08d2ef3556099766b1347b4ff2bc40669bea07d66aa06c30fe3873fc13 8f2adcd1-4c19-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=211734 https://www.mdpi.com/1424-8220/25/12/3658 1 5cbafe3b-4c19-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=211733 Fakulteta za elektrotehniko 0 0 0