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CC BY V4.0, Open Access
Thesis
Modeling the tracking and prediction of grape phenology using machine learning
Master of Science (MS), Washington State University
2025
Abstract
Accurate forecasting of crop phenology is crucial for time-sensitive farm management decisions and for implementing mitigation strategies to reduce yield and quality during suboptimal conditions. In grapevines, phenological development involves complex interactions between environmental factors and cultivar-specific physiology, making prediction inherently challenging. Traditional process-based models rely primarily on growing degree days (GDD) derived from air temperature alone. For each cultivar, these models require independent parameters, typically derived via regression. While fairly straightforward, this approach makes assumptions about the simplicity of crop phenology, overlooking other influential factors that may affect the exact timings of the phenology cycle.
Our work distinguishes itself in three main ways: i) we leverage expanded weather data inputs (i.e., air temperature, relative humidity, dew point, precipitation, and wind speed), ii) we replace the traditional process-based approach with a gated recurrent unit (GRU), and iii) training and prediction for different cultivars are handled by the same model. Using a 20-year dataset spanning 20 grape cultivars, our model outperforms GDD-based baselines in predicting budbreak, bloom, and veraison growth stages for grapevines. Furthermore, a post-processing step is introduced to generative adaptive confidence intervals for stage forecasts, offering users a quantifiable measure of prediction uncertainty.
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Details
- Title
- Modeling the tracking and prediction of grape phenology using machine learning
- Creators
- Nathan Balcarcel
- Contributors
- Ananth Kalyanaraman (Advisor)Thomas Gilray (Committee Member)Yan Yan (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Voiland College of Engineering and Architecture
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Number of pages
- 61
- Identifiers
- 99901356976601842
- Language
- English
- Resource Type
- Thesis