Publications

@inproceedings{gorthi_modeling_2024,

title = {Modeling Grape Berry Temperature for Effective Heat Stress Management in Vineyards},

author = {Srikanth Gorthi and Dattatray G. Bhalekar and Lav R. Khot and Markus Keller},

url = {https://ieeexplore.ieee.org/document/10948825},

doi = {10.1109/MetroAgriFor63043.2024.10948825},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

booktitle = {2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},

pages = {237\textendash241},

abstract = {This study developed a machine learning model to predict berry temperature using localized weather and soil attributes measured during the summer of 2023. Berry temperature was observed to be higher compared to canopy and air temperature during extreme heat events. A tree-based pipeline optimization tool was used to find an optimum machine learning algorithm. Amongst the tested models, Lasso Regression exhibited reasonable accuracy (R2 = 0.98) and root mean squared error of 0.93 °C on the test dataset.},

keywords = {Atmospheric modeling, Berry Temperature, Lasso Regression, Pipelines, Random Forest, Random forests, Real-time systems, Ridge Regression, Soil, Soil measurements, Solar radiation, Stress, Temperature measurement, Water heating},

pubstate = {published},

tppubtype = {inproceedings}

}