# The impact of seasonal temperature and water transport on the growth of sunshine rose grapevines and precision irrigation strategies

**Authors:** Ranran Wang, Zhen Han, Yingxiu Li, Suya Shang, Bo Li, Xin Lu

PMC · DOI: 10.3389/fpls.2025.1607731 · Frontiers in Plant Science · 2025-10-20

## TL;DR

This study examines how seasonal temperature and water affect the growth of Sunshine Rose grapevines and explores precision irrigation strategies.

## Contribution

The study introduces machine learning models for predicting grapevine growth, outperforming traditional methods.

## Key findings

- Grapevine growth is highest in spring and summer, with slower growth in winter.
- Machine learning models like LightGBM and XGBoost predict grapevine growth more accurately than traditional methods.
- Environmental factors significantly influence grapevine physiology and growth patterns.

## Abstract

This study explores the seasonal variations in grapevine growth and sap flow, with a particular focus on how environmental factors influence key growth indicators. Grapevines are highly sensitive to seasonal changes, and understanding these variations is essential for optimizing vineyard management practices. Given the increasing importance of precision agriculture, high-precision sensors were employed to monitor sap flow, leaf temperatures, and ambient temperature over the course of a year. By collecting data on these physiological indicators, we aim to identify patterns that can improve our understanding of grapevine responses to environmental changes. Our findings reveal significant seasonal fluctuations in grapevine growth, with the most growth occurring during the warmer months (spring and summer) and slower growth in winter. The comparison of predictive models, including Prophet, LightGBM, and XGBoost, demonstrated that machine learning models were more accurate in predicting grapevine growth compared to traditional methods. These results offer important insights into the relationship between grapevine physiology and environmental conditions, providing a foundation for improving vineyard management practices. The grape variety utilized in this study is Sunshine Rose (Shine Muscat), known for its distinctive sweet flavor and high economic value, making it a popular cultivar in vineyards worldwide.

## Linked entities

- **Species:** Vitis vinifera (taxon 29760)

## Full-text entities

- **Chemicals:** chlorophyll (MESH:D002734), Nitrogen (MESH:D009584), phosphate (MESH:D010710), phosphorus (MESH:D010758), potassium (MESH:D011188), sugar (MESH:D000073893), nitrate (MESH:D009566), water (MESH:D014867)
- **Species:** Vitis vinifera (wine grape, species) [taxon 29760]

## Full text

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## Figures

12 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12580307/full.md

## References

26 references — full list in the complete paper: https://tomesphere.com/paper/PMC12580307/full.md

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Source: https://tomesphere.com/paper/PMC12580307