2024
1.
Alan Fern; Margaret Burnett; Joseph Davidson; Janardhan Rao Doppa; Paola Pesantez-Cabrera; Ananth Kalyanaraman
AgAID Institute—AI for agricultural labor and decision support Journal Article
In: AI Magazine, vol. n/a, no. n/a, 2024, ISSN: 2371-9621, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12156).
Abstract | Links | BibTeX | Tags: AI, Farm Ops, Humans, Labor, Water
@article{fern_agaid_nodate,
title = {AgAID Institute\textemdashAI for agricultural labor and decision support},
author = {Alan Fern and Margaret Burnett and Joseph Davidson and Janardhan Rao Doppa and Paola Pesantez-Cabrera and Ananth Kalyanaraman},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aaai.12156},
doi = {10.1002/aaai.12156},
issn = {2371-9621},
year = {2024},
date = {2024-02-16},
urldate = {2024-02-16},
journal = {AI Magazine},
volume = {n/a},
number = {n/a},
abstract = {The AgAID Institute is a National AI Research Institute focused on developing AI solutions for specialty crop agriculture. Specialty crops include a variety of fruits and vegetables, nut trees, grapes, berries, and different types of horticultural crops. In the United States, the specialty crop industry accounts for a multibillion dollar industry with over 300 crops grown just along the U.S. west coast. Specialty crop agriculture presents several unique challenges: they are labor-intensive, are easily impacted by weather extremities, and are grown mostly on irrigated lands and hence are dependent on water. The AgAID Institute aims to develop AI solutions to address these challenges, particularly in the face of workforce shortages, water scarcity, and extreme weather events. Addressing this host of challenges requires advancing foundational AI research, including spatio-temporal system modeling, robot sensing and control, multiscale site-specific decision support, and designing effective human\textendashAI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12156},
keywords = {AI, Farm Ops, Humans, Labor, Water},
pubstate = {published},
tppubtype = {article}
}
The AgAID Institute is a National AI Research Institute focused on developing AI solutions for specialty crop agriculture. Specialty crops include a variety of fruits and vegetables, nut trees, grapes, berries, and different types of horticultural crops. In the United States, the specialty crop industry accounts for a multibillion dollar industry with over 300 crops grown just along the U.S. west coast. Specialty crop agriculture presents several unique challenges: they are labor-intensive, are easily impacted by weather extremities, and are grown mostly on irrigated lands and hence are dependent on water. The AgAID Institute aims to develop AI solutions to address these challenges, particularly in the face of workforce shortages, water scarcity, and extreme weather events. Addressing this host of challenges requires advancing foundational AI research, including spatio-temporal system modeling, robot sensing and control, multiscale site-specific decision support, and designing effective human–AI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.
2023
2.
Jing Wang; Tyler Hallman; Laurel Hopkins; John Burns Kilbride; W. Douglas Robinson; Rebecca Hutchinson
Model Evaluation for Geospatial Problems Proceedings Article
In: 2023.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@inproceedings{wang_model_2023,
title = {Model Evaluation for Geospatial Problems},
author = {Jing Wang and Tyler Hallman and Laurel Hopkins and John Burns Kilbride and W. Douglas Robinson and Rebecca Hutchinson},
url = {https://openreview.net/forum?id=z5dAdYOgbs\&referrer=%5Bthe%20profile%20of%20Jing%20Wang%5D(%2Fprofile%3Fid%3D~Jing_Wang38)},
year = {2023},
date = {2023-12-01},
urldate = {2023-12-01},
abstract = {Geospatial problems often involve spatial autocorrelation and covariate shift, which violate the independent, identically distributed assumption underlying standard cross-validation. In this work, we establish a theoretical criterion for unbiased cross-validation, introduce a preliminary categorization framework to guide practitioners in choosing suitable cross-validation strategies for geospatial problems, reconcile conflicting recommendations on best practices, and develop a novel, straightforward method with both theoretical guarantees and empirical success.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {inproceedings}
}
Geospatial problems often involve spatial autocorrelation and covariate shift, which violate the independent, identically distributed assumption underlying standard cross-validation. In this work, we establish a theoretical criterion for unbiased cross-validation, introduce a preliminary categorization framework to guide practitioners in choosing suitable cross-validation strategies for geospatial problems, reconcile conflicting recommendations on best practices, and develop a novel, straightforward method with both theoretical guarantees and empirical success.
2022
3.
Aseem Saxena; Paola Pesantez-Cabrera; Rohan Ballapragada; Kin-Ho Lam; Markus Keller; Alan Fern
Grape Cold Hardiness Prediction via Multi-Task Learning Workshop
Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022), 2022.
Abstract | BibTeX | Tags: AI, Cold Hardiness, Farm Ops
@workshop{saxena_grape_2022,
title = {Grape Cold Hardiness Prediction via Multi-Task Learning},
author = {Aseem Saxena and Paola Pesantez-Cabrera and Rohan Ballapragada and Kin-Ho Lam and Markus Keller and Alan Fern},
year = {2022},
date = {2022-09-01},
urldate = {2022-09-01},
journal = {Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022)},
publisher = {Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS2022)},
abstract = {Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures, such as, sprinklers, heaters, and wind machines, when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.},
keywords = {AI, Cold Hardiness, Farm Ops},
pubstate = {published},
tppubtype = {workshop}
}
Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures, such as, sprinklers, heaters, and wind machines, when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.
4.
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.
5.
Kalyanaraman, Ananth; Burnett, Margaret; Fern, Alan; Khot, Lav; Viers, Joshua
Special report: The AgAID AI institute for transforming workforce and decision support in agriculture Journal Article
In: Computers and Electronics in Agriculture, vol. 197, pp. 106944, 2022, ISSN: 0168-1699.
Abstract | Links | BibTeX | Tags: AI, Education, Farm Ops, Humans, Labor, Water
@article{kalyanaraman_special_2022,
title = {Special report: The AgAID AI institute for transforming workforce and decision support in agriculture},
author = {Kalyanaraman, Ananth and Burnett, Margaret and Fern, Alan and Khot, Lav and Viers, Joshua},
url = {https://www.sciencedirect.com/science/article/pii/S0168169922002617},
doi = {10.1016/j.compag.2022.106944},
issn = {0168-1699},
year = {2022},
date = {2022-06-01},
urldate = {2022-08-16},
journal = {Computers and Electronics in Agriculture},
volume = {197},
pages = {106944},
abstract = {Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior \textendash calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.},
keywords = {AI, Education, Farm Ops, Humans, Labor, Water},
pubstate = {published},
tppubtype = {article}
}
Tackling the grand challenges of 21st century agriculture (Ag) will require a fundamental shift in the way we envision the role of artificial intelligence (AI) technologies, and in the way we build agricultural AI systems. This shift is needed especially for complex, high-value agricultural ecosystems such as those in the Western U.S., where 300+ crops are grown. Farmers and policy makers in this region face variable profitability, major crop loss and poor crop quality owing to several challenges, including increased labor costs and shortages of skilled workers, weather and management uncertainties, and water scarcity. While AI is expected to be a significant tool for addressing these challenges, AI capabilities must be expanded and will need to account for human input and human behavior – calling for a strong AI-Ag coalition that also creates new opportunities to achieve sustained innovation. Accomplishing this goal goes well beyond the scope of any specific research project or disciplinary silo and requires a more holistic transdisciplinary effort in research, development, and training. To respond to this need, we initiated the AgAID Institute, a multi-institution, transdisciplinary National AI Research Institute that will build new public-private partnerships involving a diverse range of stakeholders in both agriculture and AI. The institute focuses its efforts on providing AI solutions to specialty crop agriculture where the challenges pertaining to water availability, climate variability and extreme weather, and labor shortages, are all significantly pronounced. Our approach to all AgAID Institute activities is being guided by three cross-cutting principles: (i) adoption as a first principle in AI design; (ii) adaptability to changing environments and scales, and (iii) amplification of human skills and machine efficiency. The AgAID Institute is conducting a range of activities including: using agricultural AI applications as testbeds for developing innovative AI technologies and workflows; laying the technological foundations for climate-smart agriculture; serving as a nexus for culturally inclusive collaborative and transdisciplinary learning and knowledge co-production; preparing the next generation workforce for careers at the intersection of Ag and AI technology; and facilitating technology adoption and transfer.
