Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes
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 pr...
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MDPI AG
2025-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1060 |
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| author | Yunlong Ma Jinyue Yang Yibo Chen Ping Wang Qinming Sun |
| author_facet | Yunlong Ma Jinyue Yang Yibo Chen Ping Wang Qinming Sun |
| author_sort | Yunlong Ma |
| collection | DOAJ |
| description | 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 R<sup>2</sup><sub>branches</sub> = 0.92, R<sup>2</sup><sub>20 cm</sub> = 0.99, and R<sup>2</sup><sub>40 cm</sub> = 0.99 and insulation blanket R<sup>2</sup><sub>branches</sub> = 0.89, R<sup>2</sup><sub>20 cm</sub> = 0.98, and R<sup>2</sup><sub>40 cm</sub> = 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. |
| format | Article |
| id | doaj-art-e08f5acfb5534d4a8df028366ec22f10 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-e08f5acfb5534d4a8df028366ec22f102025-08-20T01:57:00ZengMDPI AGAgronomy2073-43952025-04-01155106010.3390/agronomy15051060Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine GrapesYunlong Ma0Jinyue Yang1Yibo Chen2Ping Wang3Qinming Sun4Agricultural College, Shihezi University, Shihezi 832003, ChinaAgricultural College, Shihezi University, Shihezi 832003, ChinaAgricultural College, Shihezi University, Shihezi 832003, ChinaFood College, Shihezi University, Shihezi 832000, ChinaAgricultural College, Shihezi University, Shihezi 832003, ChinaGrapevines 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 R<sup>2</sup><sub>branches</sub> = 0.92, R<sup>2</sup><sub>20 cm</sub> = 0.99, and R<sup>2</sup><sub>40 cm</sub> = 0.99 and insulation blanket R<sup>2</sup><sub>branches</sub> = 0.89, R<sup>2</sup><sub>20 cm</sub> = 0.98, and R<sup>2</sup><sub>40 cm</sub> = 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.https://www.mdpi.com/2073-4395/15/5/1060insulationneural networkviticultureoverwintering |
| spellingShingle | Yunlong Ma Jinyue Yang Yibo Chen Ping Wang Qinming Sun Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes Agronomy insulation neural network viticulture overwintering |
| title | Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes |
| title_full | Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes |
| title_fullStr | Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes |
| title_full_unstemmed | Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes |
| title_short | Neural-Network-Based Prediction of Non-Burial Overwintering Material Covering Height for Wine Grapes |
| title_sort | neural network based prediction of non burial overwintering material covering height for wine grapes |
| topic | insulation neural network viticulture overwintering |
| url | https://www.mdpi.com/2073-4395/15/5/1060 |
| work_keys_str_mv | AT yunlongma neuralnetworkbasedpredictionofnonburialoverwinteringmaterialcoveringheightforwinegrapes AT jinyueyang neuralnetworkbasedpredictionofnonburialoverwinteringmaterialcoveringheightforwinegrapes AT yibochen neuralnetworkbasedpredictionofnonburialoverwinteringmaterialcoveringheightforwinegrapes AT pingwang neuralnetworkbasedpredictionofnonburialoverwinteringmaterialcoveringheightforwinegrapes AT qinmingsun neuralnetworkbasedpredictionofnonburialoverwinteringmaterialcoveringheightforwinegrapes |