Team
- Faculty: Lav Khot (WSU), Markus Keller (WSU), Bernardita Sallato (WSU), Alan Fern (OSU)
- Graduate students: Basavaraj Amogi (WSU), Dattatray Bhalekar (WSU), Srikant Gorthi (WSU)
- Undergraduate students: Nelson Goosman (WSU), Ali Alsmael (WSU)
Objectives
- Develop a cultivar-specific dataset with due ground-truthing to model heat stress in apples and grapes
- Develop and implement weather and crop physiology-driven machine learning models to predict heat stress in fresh market apples (cv. Honeycrisp, Cosmic Crisp) and grapes (cv. Chardonnay)
Hypothesis
- Machine learning models that ingest weather (open- or in-field) and crop/fruit physiology data to predict heat stress in apples and grapes will be able to address related complexities with existing energy balance and other modeling approaches, providing a reliable decision support tool for growers to timely mitigate the heat stress for improved fruit quality and packouts.
Related Publications
2024
1.
Modeling Grape Berry Temperature for Effective Heat Stress Management in Vineyards Proceedings Article
In: 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 237–241, 2024.
2023
2.
Apple fruit surface temperature prediction using weather data-driven machine learning models Proceedings Article
In: 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 429-433, 2023.
