Automated Heat Stress Mitigation

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

2023

1.

Nelson D. Goosman; Basavaraj R. Amogi; Lav R. Khot

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.

Links | BibTeX

@inproceedings{10424249,

title = {Apple fruit surface temperature prediction using weather data-driven machine learning models},

author = {Nelson D. Goosman and Basavaraj R. Amogi and Lav R. Khot},

url = {https://ieeexplore.ieee.org/document/10424249},

doi = {10.1109/MetroAgriFor58484.2023.10424249},

year  = {2023},

date = {2023-11-08},

urldate = {2023-11-08},

booktitle = {2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},

pages = {429-433},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

• https://ieeexplore.ieee.org/document/10424249
• doi:10.1109/MetroAgriFor58484.2023.10424249

Close