2023
1.
Welankar Sejal; Paola Pesantez-Cabrera; Bala Krishnamoorthy; Ananth Kalyanaraman
Persistent Homology to Study Cold Hardiness of Grape Cultivars Workshop
2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS), arXiv, 2023.
Links | BibTeX | Tags: Cold Hardiness, Computer Science , Topological Data Analysis
@workshop{welankar_grape_2023,
title = {Persistent Homology to Study Cold Hardiness of Grape Cultivars},
author = {Welankar Sejal and Paola Pesantez-Cabrera and Bala Krishnamoorthy and Ananth Kalyanaraman},
url = {https://openreview.net/pdf?id=PPoe26Ys-j},
doi = {https://doi.org/10.48550/arXiv.2302.05600},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS)},
publisher = {arXiv},
keywords = {Cold Hardiness, Computer Science , Topological Data Analysis},
pubstate = {published},
tppubtype = {workshop}
}
2022
2.
Sejal Welankar; Paola Pesantez-Cabrera; Ananth Kalyanaraman
Extracting patterns in cold hardiness behavior using topological data analysis Workshop
arXiv, 2022.
Abstract | Links | BibTeX | Tags: AI, Cold Hardiness, Farm Ops, Topological Data Analysis
@workshop{welankar_grape_2022,
title = {Extracting patterns in cold hardiness behavior using topological data analysis},
author = {Sejal Welankar and Paola Pesantez-Cabrera and Ananth Kalyanaraman},
url = {https://drive.google.com/file/d/1Mv4rGB1OhnK5Q0W_9To8UkSoqmPkL2WZ/view?usp=share_link},
year = {2022},
date = {2022-09-01},
urldate = {2022-09-01},
journal = {Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022)},
publisher = {arXiv},
abstract = {Prevention of cold injury is essential to maximize throughput for perennial specialty crops such as apples, cherries, wine grapes, etc. To achieve this, it is primordial to study the effects of environmental factors and their variations across different cultivars. To fully analyze and understand the relationship between phenotypes, genotypes, and environmental variables we need high dimensional datasets containing information such as crop height, growth characteristics, photosynthetic activity, and temperature, humidity, soil temperature. However, these datasets usually are incomplete and noisy. Topological data analysis (TDA) provides a general framework to analyze such data, extracting the underlying shape of data. The two main approaches in TDA are the mapper algorithm and persistence homology.},
keywords = {AI, Cold Hardiness, Farm Ops, Topological Data Analysis},
pubstate = {published},
tppubtype = {workshop}
}
Prevention of cold injury is essential to maximize throughput for perennial specialty crops such as apples, cherries, wine grapes, etc. To achieve this, it is primordial to study the effects of environmental factors and their variations across different cultivars. To fully analyze and understand the relationship between phenotypes, genotypes, and environmental variables we need high dimensional datasets containing information such as crop height, growth characteristics, photosynthetic activity, and temperature, humidity, soil temperature. However, these datasets usually are incomplete and noisy. Topological data analysis (TDA) provides a general framework to analyze such data, extracting the underlying shape of data. The two main approaches in TDA are the mapper algorithm and persistence homology.
