Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes
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| Publicado en: | Agronomy vol. 15, no. 5 (2025), p. 1060 |
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2073-4395 | ||
| 024 | 7 | |a 10.3390/agronomy15051060 |2 doi | |
| 035 | |a 3211847027 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231332 |2 nlm | ||
| 100 | 1 | |a Ma, Yunlong |u Agricultural College, Shihezi University, Shihezi 832003, China; yunlong@stu.shzu.edu.cn (Y.M.); 20231012342@stu.shzu.edu.cn (Y.C.) | |
| 245 | 1 | |a Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Grapevines in cold regions are prone to frost damage in winter. Due to its adverse effects on soil structure, plant damage, high operational costs, and limited mechanization feasibility, buried soil overwintering has been gradually replaced by no-burial overwintering techniques, which are now the primary focus for mitigating frost damage in wine grapes. While current research focuses on the selection of thermal insulation materials, less attention has been paid to the insulation mechanism of covering materials and covering methods. In this study, we investigated the insulation performance of two covering materials (tarpaulin and insulation blanket) combined with six height treatments (5–30 cm) to analyze the effect of insulation space volume on no-buried-soil overwintering. The results show that the thermal insulation performance of the insulation blanket is significantly better than that of the tarpaulin. The 5 cm height treatment under the tarpaulin cover and the 25 cm height treatment under the insulation blanket cover exhibited the best thermal insulation performance. Using a neural network machine learning approach, we constructed a model related to the height of the insulation material and facilitate the model’s accurate predictions, in which tarpaulin R2branches = 0.92, R220 cm = 0.99, and R240 cm = 0.99 and insulation blanket R2branches = 0.89, R220 cm = 0.98, and R240 cm = 0.99. The model predicted optimal insulation heights of 6 cm for the tarpaulin and 22 cm for the insulation blanket. Factors like solar radiation within the insulation space, ground radiation, airflow, and material thermal conductivity affect the optimal insulation height for different materials. This study used a neural network model to predict the optimal insulation heights for different materials, providing systematic theoretical guidance for the overwintering cultivation of wine grapes and aiding the safe development of the wine grape industry in cold regions. | |
| 651 | 4 | |a Tien Shan Mountains | |
| 651 | 4 | |a China | |
| 653 | |a Wines | ||
| 653 | |a Cold | ||
| 653 | |a Thermal conductivity | ||
| 653 | |a Air flow | ||
| 653 | |a Frost | ||
| 653 | |a Buried structures | ||
| 653 | |a Machine learning | ||
| 653 | |a Fruits | ||
| 653 | |a Thermal insulation | ||
| 653 | |a Radiation | ||
| 653 | |a Cold regions | ||
| 653 | |a Polyethylene | ||
| 653 | |a Solar radiation | ||
| 653 | |a Grapes | ||
| 653 | |a Soil structure | ||
| 653 | |a Berries | ||
| 653 | |a Overwintering | ||
| 653 | |a Cultivation | ||
| 653 | |a Humidity | ||
| 653 | |a Cotton | ||
| 653 | |a Frost damage | ||
| 653 | |a Trends | ||
| 653 | |a Fruit cultivation | ||
| 653 | |a Mechanization | ||
| 653 | |a Time series | ||
| 653 | |a Heat conductivity | ||
| 653 | |a Wine | ||
| 653 | |a Neural networks | ||
| 653 | |a Grapevines | ||
| 653 | |a Temperature | ||
| 653 | |a Insulation | ||
| 653 | |a Tarpaulins | ||
| 653 | |a Vitaceae | ||
| 700 | 1 | |a Yang, Jinyue |u Agricultural College, Shihezi University, Shihezi 832003, China; yunlong@stu.shzu.edu.cn (Y.M.); 20231012342@stu.shzu.edu.cn (Y.C.) | |
| 700 | 1 | |a Chen, Yibo |u Agricultural College, Shihezi University, Shihezi 832003, China; yunlong@stu.shzu.edu.cn (Y.M.); 20231012342@stu.shzu.edu.cn (Y.C.) | |
| 700 | 1 | |a Wang, Ping |u Food College, Shihezi University, Shihezi 832000, China | |
| 700 | 1 | |a Sun Qinming |u Agricultural College, Shihezi University, Shihezi 832003, China; yunlong@stu.shzu.edu.cn (Y.M.); 20231012342@stu.shzu.edu.cn (Y.C.) | |
| 773 | 0 | |t Agronomy |g vol. 15, no. 5 (2025), p. 1060 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211847027/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211847027/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211847027/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |