2024
11.
Rosalinda Garcia; Patricia Morreale; Gail Verdi; Heather Garcia; Geraldine Jimena Noa; Spencer P. Madsen; Maria Jesus Alzugaray-Orellana; Elizabeth Li; Margaret Burnett
The Matchmaker Inclusive Design Curriculum: A Faculty-Enabling Curriculum to Teach Inclusive Design Throughout Undergraduate CS Proceedings Article
In: Proceedings of the CHI Conference on Human Factors in Computing Systems, pp. 1–22, Association for Computing Machinery, New York, NY, USA, 2024, ISBN: 9798400703300.
Abstract | Links | BibTeX | Tags:
@inproceedings{garcia_matchmaker_2024,
title = {The Matchmaker Inclusive Design Curriculum: A Faculty-Enabling Curriculum to Teach Inclusive Design Throughout Undergraduate CS},
author = {Rosalinda Garcia and Patricia Morreale and Gail Verdi and Heather Garcia and Geraldine Jimena Noa and Spencer P. Madsen and Maria Jesus Alzugaray-Orellana and Elizabeth Li and Margaret Burnett},
url = {https://dl.acm.org/doi/10.1145/3613904.3642475},
doi = {10.1145/3613904.3642475},
isbn = {9798400703300},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems},
pages = {1\textendash22},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI '24},
abstract = {Despite efforts to raise awareness of societal and ethical issues in CS education, research shows students often do not act upon their new awareness (Problem 1). One such issue, well-established by HCI research, is that much of technology contains barriers impacting numerous populations\textemdashsuch as minoritized genders, races, ethnicities, and more. HCI has inclusive design methods that help\textemdashbut these skills are rarely taught, even in HCI classes (Problem 2). To address Problems 1 and 2, we created the Matchmaker Curriculum to pair CS faculty\textemdashincluding non-HCI faculty\textemdashwith inclusive design elements to allow for inclusive design skill-building throughout their CS program. We present the curriculum and a field study, in which we followed 18 faculty along their journey. The results show how the Matchmaker Curriculum equipped 88% of these faculty with enough inclusive design teaching knowledge to successfully embed actionable inclusive design skill-building into 13 CS courses.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Despite efforts to raise awareness of societal and ethical issues in CS education, research shows students often do not act upon their new awareness (Problem 1). One such issue, well-established by HCI research, is that much of technology contains barriers impacting numerous populations—such as minoritized genders, races, ethnicities, and more. HCI has inclusive design methods that help—but these skills are rarely taught, even in HCI classes (Problem 2). To address Problems 1 and 2, we created the Matchmaker Curriculum to pair CS faculty—including non-HCI faculty—with inclusive design elements to allow for inclusive design skill-building throughout their CS program. We present the curriculum and a field study, in which we followed 18 faculty along their journey. The results show how the Matchmaker Curriculum equipped 88% of these faculty with enough inclusive design teaching knowledge to successfully embed actionable inclusive design skill-building into 13 CS courses.
12.
Mohammed Amine Gharsallaoui; Bhupinderjeet Singh; Supriya Savalkar; Aryan Deshwal; Yan Yan; Ananth Kalyanaraman; Kirti Rajagopalan; Janardhan Rao Doppa
Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach Miscellaneous
2024, (arXiv:2406.00133 [cs]).
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Machine Learning
@misc{gharsallaoui_streamflow_2024,
title = {Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach},
author = {Mohammed Amine Gharsallaoui and Bhupinderjeet Singh and Supriya Savalkar and Aryan Deshwal and Yan Yan and Ananth Kalyanaraman and Kirti Rajagopalan and Janardhan Rao Doppa},
url = {http://arxiv.org/abs/2406.00133},
doi = {10.48550/arXiv.2406.00133},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
publisher = {arXiv},
abstract = {Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but using simplifying assumptions which can lead to poor accuracy. Data-driven approaches offer a powerful alternative, but they require large amount of training data and tend to produce predictions that are inconsistent with physical laws. This paper studies a constrained reasoning and learning (CRL) approach where physical laws represented as logical constraints are integrated as a layer in the deep neural network. To address small data setting, we develop a theoretically-grounded training approach to improve the generalization accuracy of deep models. For uncertainty quantification, we combine the synergistic strengths of Gaussian processes (GPs) and deep temporal models (i.e., deep models for time-series forecasting) by passing the learned latent representation as input to a standard distance-based kernel. Experiments on multiple real-world datasets demonstrate the effectiveness of both CRL and GP with deep kernel approaches over strong baseline methods.},
note = {arXiv:2406.00133 [cs]},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
pubstate = {published},
tppubtype = {misc}
}
Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but using simplifying assumptions which can lead to poor accuracy. Data-driven approaches offer a powerful alternative, but they require large amount of training data and tend to produce predictions that are inconsistent with physical laws. This paper studies a constrained reasoning and learning (CRL) approach where physical laws represented as logical constraints are integrated as a layer in the deep neural network. To address small data setting, we develop a theoretically-grounded training approach to improve the generalization accuracy of deep models. For uncertainty quantification, we combine the synergistic strengths of Gaussian processes (GPs) and deep temporal models (i.e., deep models for time-series forecasting) by passing the learned latent representation as input to a standard distance-based kernel. Experiments on multiple real-world datasets demonstrate the effectiveness of both CRL and GP with deep kernel approaches over strong baseline methods.
13.
Shubhomoy Das; Md Rakibul Islam; Nitthilan Kannappan Jayakodi; Janardhan Rao Doppa
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning Journal Article
In: Journal of Artificial Intelligence Research, vol. 80, pp. 127–170, 2024, ISSN: 1076-9757.
Abstract | Links | BibTeX | Tags: knowledge discovery, Machine Learning
@article{das_effectiveness_2024,
title = {Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning},
author = {Shubhomoy Das and Md Rakibul Islam and Nitthilan Kannappan Jayakodi and Janardhan Rao Doppa},
url = {https://www.jair.org/index.php/jair/article/view/14741},
doi = {10.1613/jair.1.14741},
issn = {1076-9757},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
journal = {Journal of Artificial Intelligence Research},
volume = {80},
pages = {127\textendash170},
abstract = {Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies.
This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.},
keywords = {knowledge discovery, Machine Learning},
pubstate = {published},
tppubtype = {article}
}
Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies.
This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.
This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.
14.
Deanna Flynn; Abhinav Jain; Heather Knight; Cristina G. Wilson; Cindy Grimm
Uncovering implementable dormant pruning decisions from three different stakeholder perspectives Miscellaneous
2024, (arXiv:2405.04030 [cs]).
Abstract | Links | BibTeX | Tags: Computer Science - Robotics
@misc{flynn_uncovering_2024,
title = {Uncovering implementable dormant pruning decisions from three different stakeholder perspectives},
author = {Deanna Flynn and Abhinav Jain and Heather Knight and Cristina G. Wilson and Cindy Grimm},
url = {http://arxiv.org/abs/2405.04030},
doi = {10.48550/arXiv.2405.04030},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
publisher = {arXiv},
abstract = {Dormant pruning, or the removal of unproductive portions of a tree while a tree is not actively growing, is an important orchard task to help maintain yield, requiring years to build expertise. Because of long training periods and an increasing labor shortage in agricultural jobs, pruning could benefit from robotic automation. However, to program robots to prune branches, we first need to understand how pruning decisions are made, and what variables in the environment (e.g., branch size and thickness) we need to capture. Working directly with three pruning stakeholders \textendash horticulturists, growers, and pruners \textendash we find that each group of human experts approaches pruning decision-making differently. To capture this knowledge, we present three studies and two extracted pruning protocols from field work conducted in Prosser, Washington in January 2022 and 2023. We interviewed six stakeholders (two in each group) and observed pruning across three cultivars \textendash Bing Cherries, Envy Apples, and Jazz Apples \textendash and two tree architectures \textendash Upright Fruiting Offshoot and V-Trellis. Leveraging participant interviews and video data, this analysis uses grounded coding to extract pruning terminology, discover horticultural contexts that influence pruning decisions, and find implementable pruning heuristics for autonomous systems. The results include a validated terminology set, which we offer for use by both pruning stakeholders and roboticists, to communicate general pruning concepts and heuristics. The results also highlight seven pruning heuristics utilizing this terminology set that would be relevant for use by future autonomous robot pruning systems, and characterize three discovered horticultural contexts (i.e., environmental management, crop-load management, and replacement wood) across all three cultivars.},
note = {arXiv:2405.04030 [cs]},
keywords = {Computer Science - Robotics},
pubstate = {published},
tppubtype = {misc}
}
Dormant pruning, or the removal of unproductive portions of a tree while a tree is not actively growing, is an important orchard task to help maintain yield, requiring years to build expertise. Because of long training periods and an increasing labor shortage in agricultural jobs, pruning could benefit from robotic automation. However, to program robots to prune branches, we first need to understand how pruning decisions are made, and what variables in the environment (e.g., branch size and thickness) we need to capture. Working directly with three pruning stakeholders – horticulturists, growers, and pruners – we find that each group of human experts approaches pruning decision-making differently. To capture this knowledge, we present three studies and two extracted pruning protocols from field work conducted in Prosser, Washington in January 2022 and 2023. We interviewed six stakeholders (two in each group) and observed pruning across three cultivars – Bing Cherries, Envy Apples, and Jazz Apples – and two tree architectures – Upright Fruiting Offshoot and V-Trellis. Leveraging participant interviews and video data, this analysis uses grounded coding to extract pruning terminology, discover horticultural contexts that influence pruning decisions, and find implementable pruning heuristics for autonomous systems. The results include a validated terminology set, which we offer for use by both pruning stakeholders and roboticists, to communicate general pruning concepts and heuristics. The results also highlight seven pruning heuristics utilizing this terminology set that would be relevant for use by future autonomous robot pruning systems, and characterize three discovered horticultural contexts (i.e., environmental management, crop-load management, and replacement wood) across all three cultivars.
15.
Adrienne M. Marshall; John T. Abatzoglou; Stefan Rahimi; Dennis P. Lettenmaier; Alex Hall
California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change Journal Article
In: Proceedings of the National Academy of Sciences, vol. 121, no. 20, pp. e2320600121, 2024, (Publisher: Proceedings of the National Academy of Sciences).
Abstract | Links | BibTeX | Tags:
@article{marshall_californias_2024,
title = {California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change},
author = {Adrienne M. Marshall and John T. Abatzoglou and Stefan Rahimi and Dennis P. Lettenmaier and Alex Hall},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.2320600121},
doi = {10.1073/pnas.2320600121},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {121},
number = {20},
pages = {e2320600121},
abstract = {The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term “snow deluge” and use anomalously high snowpack in California’s Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California’s 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges\textemdashwhich we define as the 20 y April 1 SWE event\textemdashare projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.},
note = {Publisher: Proceedings of the National Academy of Sciences},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term “snow deluge” and use anomalously high snowpack in California’s Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California’s 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges—which we define as the 20 y April 1 SWE event—are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation.
16.
Victor Blanco; Lee Kalcsits
In: Frontiers in Plant Science, vol. 15, 2024, ISSN: 1664-462X, (Publisher: Frontiers).
Abstract | Links | BibTeX | Tags: Continuous measurements, fruit growth, Plant-based sensors, Precision irrigation, Tree water status indicators, water potential
@article{blanco_relating_2024,
title = {Relating microtensiometer-based trunk water potential with sap flow, canopy temperature, and trunk and fruit diameter variations for irrigated ‘Honeycrisp’ apple},
author = {Victor Blanco and Lee Kalcsits},
url = {https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1393028/full},
doi = {10.3389/fpls.2024.1393028},
issn = {1664-462X},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
journal = {Frontiers in Plant Science},
volume = {15},
abstract = {\<p\>Instrumentation plays a key role in modern horticulture. Thus, the microtensiomenter, a new plant-based sensor that continuously monitors trunk water potential (Ψ$_textrmtrunk$) can help in irrigation management decisions. To compare the response of the Ψ$_textrmtrunk$ with other continuous tree water status indicators such as the sap flow rate, the difference between canopy and air temperatures, or the variations of the trunk and fruit diameter, all the sensors were installed in 2022 in a commercial orchard of ‘Honeycrisp’ apple trees with M.9 rootstocks in Washinton State (USA). From the daily evolution of the Ψ$_textrmtrunk$, five indicators were considered: predawn, midday, minimum, daily mean, and daily range (the difference between the daily maximum and minimum values). The daily range of Ψ$_textrmtrunk$ was the most linked to the maximum daily shrinkage (MDS; R$^textrm2$ = 0.42), the canopy-to-air temperature (Tc-Ta; R$^textrm2$ = 0.32), and the sap flow rate (SF; R$^textrm2$ = 0.30). On the other hand, the relative fruit growth rate (FRGR) was more related to the minimum Ψ$_textrmtrunk$ (R$^textrm2$ = 0.33) and the daily mean Ψ$_textrmtrunk$ (R$^textrm2$ = 0.32) than to the daily range of Ψ$_textrmtrunk$. All indicators derived from Ψ$_textrmtrunk$ identified changes in tree water status after each irrigation event and had low coefficients of variation and high sensitivity. These results encourage Ψ$_textrmtrunk$ as a promising candidate for continuous monitoring of tree water status, however, more research is needed to better relate these measures with other widely studied plant-based indicators and identify good combinations of sensors and threshold values.\</p\>},
note = {Publisher: Frontiers},
keywords = {Continuous measurements, fruit growth, Plant-based sensors, Precision irrigation, Tree water status indicators, water potential},
pubstate = {published},
tppubtype = {article}
}
<p>Instrumentation plays a key role in modern horticulture. Thus, the microtensiomenter, a new plant-based sensor that continuously monitors trunk water potential (Ψ$_textrmtrunk$) can help in irrigation management decisions. To compare the response of the Ψ$_textrmtrunk$ with other continuous tree water status indicators such as the sap flow rate, the difference between canopy and air temperatures, or the variations of the trunk and fruit diameter, all the sensors were installed in 2022 in a commercial orchard of ‘Honeycrisp’ apple trees with M.9 rootstocks in Washinton State (USA). From the daily evolution of the Ψ$_textrmtrunk$, five indicators were considered: predawn, midday, minimum, daily mean, and daily range (the difference between the daily maximum and minimum values). The daily range of Ψ$_textrmtrunk$ was the most linked to the maximum daily shrinkage (MDS; R$^textrm2$ = 0.42), the canopy-to-air temperature (Tc-Ta; R$^textrm2$ = 0.32), and the sap flow rate (SF; R$^textrm2$ = 0.30). On the other hand, the relative fruit growth rate (FRGR) was more related to the minimum Ψ$_textrmtrunk$ (R$^textrm2$ = 0.33) and the daily mean Ψ$_textrmtrunk$ (R$^textrm2$ = 0.32) than to the daily range of Ψ$_textrmtrunk$. All indicators derived from Ψ$_textrmtrunk$ identified changes in tree water status after each irrigation event and had low coefficients of variation and high sensitivity. These results encourage Ψ$_textrmtrunk$ as a promising candidate for continuous monitoring of tree water status, however, more research is needed to better relate these measures with other widely studied plant-based indicators and identify good combinations of sensors and threshold values.</p>
17.
Mark Jacob Schrader; Lav R Khot
Smart Vineyard Concepts to Reality in Washington State Booklet
Viticulture and Enology Extension News, 2024.
@booklet{schrader_smart_nodate,
title = {Smart Vineyard Concepts to Reality in Washington State},
author = {Mark Jacob Schrader and Lav R Khot},
url = {https://s3-us-west-2.amazonaws.com/sites.cahnrs.wsu.edu/wp-content/uploads/sites/66/2024/04/19133949/2024-SpringVEEN-FINAL.pdf},
year = {2024},
date = {2024-04-25},
address = {Viticulture and Enology Extension News},
month = {04},
keywords = {},
pubstate = {published},
tppubtype = {booklet}
}
18.
Supriya Savalkar; Md. Redwan Ahmad Khan; Bhupinderjeet Singh; Matt Pruett; R. Troy Peters; Claudio O Stöckle; Sean E. Hill; Kirti Rajagopalan
Errors in temporal disaggregation of temperature can lead to non-negligible biases in agroecosystem risk assessment Journal Article
In: Agricultural and Forest Meteorology, vol. 349, pp. 109952, 2024, ISSN: 0168-1923.
Abstract | Links | BibTeX | Tags: Agroecosystems modeling, Input error propagation into models, Radiation disaggregation, Temperature disaggregation, Temperature disaggregation error adjustment
@article{savalkar_errors_2024,
title = {Errors in temporal disaggregation of temperature can lead to non-negligible biases in agroecosystem risk assessment},
author = {Supriya Savalkar and Md. Redwan Ahmad Khan and Bhupinderjeet Singh and Matt Pruett and R. Troy Peters and Claudio O St\"{o}ckle and Sean E. Hill and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0168192324000674},
doi = {10.1016/j.agrformet.2024.109952},
issn = {0168-1923},
year = {2024},
date = {2024-04-01},
urldate = {2024-04-01},
journal = {Agricultural and Forest Meteorology},
volume = {349},
pages = {109952},
abstract = {Models are crucial for simulating complex systems and decision-making, but they have uncertainties that must be characterized and understood. One uncertainty that has been overlooked in agroecosystem assessments is that arising from the temporal disaggregation of temperature and solar radiation. Our study used data from an agricultural weather station network to investigate (a) the errors associated with hourly temporal disaggregation of daily temperatures and solar radiation, (b) how these input errors impact two agroecosystem models, (c) the sensitivity of change assessments to disaggregation errors, and (d) how high-temporal-resolution weather station networks can be leveraged to correct disaggregation errors in daily gridded meteorological data products. Our findings demonstrate that temporal temperature disaggregation errors can have a significant impact on agroecosystem model output, with large errors in sunburn risk estimation (\>100% median deviation percentage) but minimal effects on chill accumulation (\<5% median deviation percentage). However, we were able to achieve significant reductions in error (\>75% error reduction in sunburn risk assessment in majority of cases) by integrating simple monthly statistics from station observations into the disaggregation process. Our study highlights the importance of understanding uncertainties in agroecosystem models stemming from temporal disaggregation of temperature, and the potential benefits of utilizing simple adjustments leveraging weather station networks to improve model accuracy and applicability for decision-making.},
keywords = {Agroecosystems modeling, Input error propagation into models, Radiation disaggregation, Temperature disaggregation, Temperature disaggregation error adjustment},
pubstate = {published},
tppubtype = {article}
}
Models are crucial for simulating complex systems and decision-making, but they have uncertainties that must be characterized and understood. One uncertainty that has been overlooked in agroecosystem assessments is that arising from the temporal disaggregation of temperature and solar radiation. Our study used data from an agricultural weather station network to investigate (a) the errors associated with hourly temporal disaggregation of daily temperatures and solar radiation, (b) how these input errors impact two agroecosystem models, (c) the sensitivity of change assessments to disaggregation errors, and (d) how high-temporal-resolution weather station networks can be leveraged to correct disaggregation errors in daily gridded meteorological data products. Our findings demonstrate that temporal temperature disaggregation errors can have a significant impact on agroecosystem model output, with large errors in sunburn risk estimation (>100% median deviation percentage) but minimal effects on chill accumulation (<5% median deviation percentage). However, we were able to achieve significant reductions in error (>75% error reduction in sunburn risk assessment in majority of cases) by integrating simple monthly statistics from station observations into the disaggregation process. Our study highlights the importance of understanding uncertainties in agroecosystem models stemming from temporal disaggregation of temperature, and the potential benefits of utilizing simple adjustments leveraging weather station networks to improve model accuracy and applicability for decision-making.
19.
Supriya Savalkar; Md. Redwan Ahmad Khan; Bhupinderjeet Singh; Matt Pruett; R. Troy Peters; Claudio O Stöckle; Sean E. Hill; Kirti Rajagopalan
Errors in temporal disaggregation of temperature can lead to non-negligible biases in agroecosystem risk assessment Journal Article
In: Agricultural and Forest Meteorology, vol. 349, pp. 109952, 2024, ISSN: 0168-1923.
Abstract | Links | BibTeX | Tags: Agroecosystems modeling, Input error propagation into models, Radiation disaggregation, Temperature disaggregation, Temperature disaggregation error adjustment
@article{savalkar_errors_2024b,
title = {Errors in temporal disaggregation of temperature can lead to non-negligible biases in agroecosystem risk assessment},
author = {Supriya Savalkar and Md. Redwan Ahmad Khan and Bhupinderjeet Singh and Matt Pruett and R. Troy Peters and Claudio O St\"{o}ckle and Sean E. Hill and Kirti Rajagopalan},
url = {https://www.sciencedirect.com/science/article/pii/S0168192324000674},
doi = {10.1016/j.agrformet.2024.109952},
issn = {0168-1923},
year = {2024},
date = {2024-04-01},
urldate = {2024-08-09},
journal = {Agricultural and Forest Meteorology},
volume = {349},
pages = {109952},
abstract = {Models are crucial for simulating complex systems and decision-making, but they have uncertainties that must be characterized and understood. One uncertainty that has been overlooked in agroecosystem assessments is that arising from the temporal disaggregation of temperature and solar radiation. Our study used data from an agricultural weather station network to investigate (a) the errors associated with hourly temporal disaggregation of daily temperatures and solar radiation, (b) how these input errors impact two agroecosystem models, (c) the sensitivity of change assessments to disaggregation errors, and (d) how high-temporal-resolution weather station networks can be leveraged to correct disaggregation errors in daily gridded meteorological data products. Our findings demonstrate that temporal temperature disaggregation errors can have a significant impact on agroecosystem model output, with large errors in sunburn risk estimation (\>100% median deviation percentage) but minimal effects on chill accumulation (\<5% median deviation percentage). However, we were able to achieve significant reductions in error (\>75% error reduction in sunburn risk assessment in majority of cases) by integrating simple monthly statistics from station observations into the disaggregation process. Our study highlights the importance of understanding uncertainties in agroecosystem models stemming from temporal disaggregation of temperature, and the potential benefits of utilizing simple adjustments leveraging weather station networks to improve model accuracy and applicability for decision-making.},
keywords = {Agroecosystems modeling, Input error propagation into models, Radiation disaggregation, Temperature disaggregation, Temperature disaggregation error adjustment},
pubstate = {published},
tppubtype = {article}
}
Models are crucial for simulating complex systems and decision-making, but they have uncertainties that must be characterized and understood. One uncertainty that has been overlooked in agroecosystem assessments is that arising from the temporal disaggregation of temperature and solar radiation. Our study used data from an agricultural weather station network to investigate (a) the errors associated with hourly temporal disaggregation of daily temperatures and solar radiation, (b) how these input errors impact two agroecosystem models, (c) the sensitivity of change assessments to disaggregation errors, and (d) how high-temporal-resolution weather station networks can be leveraged to correct disaggregation errors in daily gridded meteorological data products. Our findings demonstrate that temporal temperature disaggregation errors can have a significant impact on agroecosystem model output, with large errors in sunburn risk estimation (>100% median deviation percentage) but minimal effects on chill accumulation (<5% median deviation percentage). However, we were able to achieve significant reductions in error (>75% error reduction in sunburn risk assessment in majority of cases) by integrating simple monthly statistics from station observations into the disaggregation process. Our study highlights the importance of understanding uncertainties in agroecosystem models stemming from temporal disaggregation of temperature, and the potential benefits of utilizing simple adjustments leveraging weather station networks to improve model accuracy and applicability for decision-making.
20.
Uddhav Bhattarai; Qin Zhang; Manoj Karkee
Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops Journal Article
In: Field Robotics, 2024, (arXiv:2304.04919 [cs]).
Abstract | Links | BibTeX | Tags: AI, Humans, Labor, Thinning
@article{bhattarai_design_2023,
title = {Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops},
author = {Uddhav Bhattarai and Qin Zhang and Manoj Karkee},
url = {http://arxiv.org/abs/2304.04919},
doi = {10.48550/arXiv.2304.04919},
year = {2024},
date = {2024-03-07},
urldate = {2024-03-07},
journal = {Field Robotics},
publisher = {arXiv},
abstract = {The US apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruit thinning, and harvesting. Blossom thinning is one of the crucial crop load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical, and mechanical thinning are available for large-scale blossom thinning such approaches often yield unpredictable thinning results and may cause damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor intensive and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for blossom thinning in apple orchards using a computer vision system with artificial intelligence, a six degrees of freedom robotic manipulator, and an electrically actuated miniature end-effector for robotic blossom thinning. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system ability to remove varying proportion of flowers from apple flower clusters. During boundary thinning the end effector was actuated around the cluster boundary while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 seconds. The boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 seconds per cluster, whereas center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 seconds per cluster. When commercially adopted, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.},
note = {arXiv:2304.04919 [cs]},
keywords = {AI, Humans, Labor, Thinning},
pubstate = {published},
tppubtype = {article}
}
The US apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruit thinning, and harvesting. Blossom thinning is one of the crucial crop load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical, and mechanical thinning are available for large-scale blossom thinning such approaches often yield unpredictable thinning results and may cause damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor intensive and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for blossom thinning in apple orchards using a computer vision system with artificial intelligence, a six degrees of freedom robotic manipulator, and an electrically actuated miniature end-effector for robotic blossom thinning. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system ability to remove varying proportion of flowers from apple flower clusters. During boundary thinning the end effector was actuated around the cluster boundary while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 seconds. The boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 seconds per cluster, whereas center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 seconds per cluster. When commercially adopted, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.
