Publications

@article{flynn_uncovering_2026,

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 = {https://journals.ashs.org/view/journals/horttech/36/2/article-p325.xml},

doi = {10.21273/HORTTECH05817-25},

year  = {2026},

date = {2026-04-01},

urldate = {2026-04-01},

chapter = {HortTechnology},

abstract = {Uncovering Implementable Dormant Pruning Decisions from Three Different Stakeholder Perspectives},

keywords = {AI, Labor},

pubstate = {published},

tppubtype = {article}

}

@article{dinkins_localized_2026,

title = {Localized Near Surface Temperature Inversion Forecasting Using Long Short-Term Memory},

author = {Taylor Dinkins and Weng-Keen Wong and Basavaraj Amogi and Paola Pesantez-Cabrera and Jaitun Patel and Lav Khot and Alan Fern},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/41462},

doi = {10.1609/aaai.v40i47.41462},

issn = {2374-3468},

year  = {2026},

date = {2026-03-01},

urldate = {2026-03-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {40},

number = {47},

pages = {40249\textendash40257},

abstract = {Near surface temperature inversions are periods in which a low layer of warm air is trapped between cooler air higher up in the atmosphere and dense cooler air below it near the surface level. By causing cooler air to pool near the surface level, inversions can have detrimental effects for crop growers, including frost, increased moisture, and pesticide drift. As a result, predicting the occurrence and magnitude of these inversions yields substantial benefits for growers. We introduce a Long Short-Term Memory (LSTM) model for temperature inversion forecasting that is able to effectively predict localized, near surface temperature inversions in advance such that growers can take actions to mitigate the detrimental effects. We show a substantial performance gain over a deployed temperature inversion forecasting system, and include a series of ablations that show the benefit of using publicly available terrain-specific feature information when modeling inversions at this scale.},

keywords = {AI, Farm Ops},

pubstate = {published},

tppubtype = {article}

}

@article{goebel_budgeted_2026,

title = {Budgeted Online Active Learning with Expert Advice and Episodic Priors},

author = {Kristen Goebel and William Solow and Paola Pesantez-Cabrera and Markus Keller and Alan Fern},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/41191},

doi = {10.1609/aaai.v40i45.41191},

issn = {2374-3468},

year  = {2026},

date = {2026-03-01},

urldate = {2026-03-01},

journal = {Proceedings of the AAAI Conference on Artificial Intelligence},

volume = {40},

number = {45},

pages = {38496\textendash38504},

abstract = {This paper introduces a novel approach to budgeted online active learning from finite-horizon data streams with extremely limited labeling budgets. In agricultural applications, such streams might include daily weather data over a growing season, and labels require costly measurements of weather-dependent plant characteristics. Our method integrates two key sources of prior information: a collection of preexisting expert predictors and episodic behavioral knowledge of the experts based on unlabeled data streams. Unlike previous research on online active learning with experts, our work simultaneously considers query budgets, finite horizons, and episodic knowledge, enabling effective learning in applications with severely limited labeling capacity. We demonstrate the utility of our approach through experiments on various prediction problems derived from both a realistic agricultural crop simulator and real-world data from multiple grape cultivars. The results show that our method significantly outperforms baseline expert predictions, uniform query selection, and existing approaches that consider budgets and limited horizons but neglect episodic knowledge, even under highly constrained labeling budgets.},

keywords = {AI, Farm Ops},

pubstate = {published},

tppubtype = {article}

}

@article{sambaturu_scenario-aware_2026,

title = {Scenario-aware control of multipathway spread processes: Application to biological invasions},

author = {Prathyush Sambaturu and Manisha Sudhir and Hongze Chen and Anil Vullikanti and Rangaswamy Muniappan and Abhijin Adiga},

url = {https://doi.org/10.1093/pnasnexus/pgag036},

doi = {10.1093/pnasnexus/pgag036},

issn = {2752-6542},

year  = {2026},

date = {2026-03-01},

urldate = {2026-03-01},

journal = {PNAS Nexus},

volume = {5},

number = {3},

pages = {pgag036},

abstract = {Optimal control of spread processes over networks is a challenging problem, even for simple diffusion models. Real-world processes\textemdashsuch as infectious disease outbreaks and biological invasions\textemdashoften involve multiple spread pathways and time-varying network dynamics. In this work, we address the problem of region-wide interventions, where the goal is to select an optimal set of regions (groups of nodes) in a network to minimize spread, subject to budget constraints, intervention delays, and a given spread scenario which reflects prior knowledge of the process\textemdashsuch as initial infection locations, parameter estimates, and other context-specific assumptions. We present a general approach based on integer linear programming and sample average approximation, applicable across a broad class of diffusion models. We also establish theoretical performance guarantees for our method within the bicriteria approximation framework. To demonstrate its effectiveness, we apply the approach to model the spread of a representative agricultural pest. Our method yields near-optimal solutions and consistently outperforms standard baselines. The results emphasize the value of scenario-specific intervention strategies, showing that early action can significantly reduce spread under limited budgets and produce stable outcomes even under model uncertainty.},

keywords = {AI, Water},

pubstate = {published},

tppubtype = {article}

}

@article{roumeliotis_agentic_2026b,

title = {Agentic AI With Orchestrator-Agent Trust: A Modular Visual Classification Framework With Trust-Aware Orchestration and RAG-Based Reasoning},

author = {Konstantinos I. Roumeliotis and Ranjan Sapkota and Manoj Karkee and Nikolaos D. Tselikas},

url = {https://ieeexplore.ieee.org/document/11373381/},

doi = {10.1109/ACCESS.2026.3662282},

issn = {2169-3536},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {IEEE Access},

volume = {14},

pages = {26965\textendash26982},

abstract = {Modern Artificial Intelligence (AI) increasingly relies on multi-agent architectures that blend visual and language understanding. Yet, a pressing challenge remains: How can we trust these agents especially in zero-shot settings with no fine-tuning? We introduce a novel modular Agentic AI visual classification framework that integrates generalist multimodal agents with a non-visual reasoning orchestrator and a Retrieval-Augmented Generation (RAG) module. Applied to apple leaf disease diagnosis, we benchmark three configurations: (I) zero-shot with confidence-based orchestration, (II) fine-tuned agents with improved performance, and (III) trust-calibrated orchestration enhanced by CLIP-based image retrieval and re-evaluation loops. Using confidence calibration metrics (ECE, OCR, CCC), the orchestrator modulates trust across agents. Our results demonstrate a 77.94% accuracy improvement in the zero-shot setting using trust-aware orchestration and RAG, achieving 85.63% overall. GPT-4o showed better calibration, while Qwen-2.5-VL displayed overconfidence. Furthermore, image-RAG grounded predictions with visually similar cases, enabling correction of agent overconfidence via iterative re-evaluation. The proposed system separates perception (vision agents) from meta-reasoning (orchestrator), enabling scalable and interpretable multi-agent AI. This blueprint illustrates how Agentic AI can deliver trustworthy, modular, and transparent reasoning, and is extensible to diagnostics, biology, and other trust-critical domains. In doing so, we highlight Agentic AI not just as an architecture but as a paradigm for building reliable multi-agent intelligence. All models, prompts, results, and system components including the complete software source code are openly released to support reproducibility, transparency, and community benchmarking at our Github page.},

keywords = {AI, Labor},

pubstate = {published},

tppubtype = {article}

}

@article{singh_incorporating_2025,

title = {Incorporating relative humidity in precipitation phase partitioning reduces model bias for some snow and streamflow metrics across the Northwest US},

author = {Bhupinderjeet Singh and Mingliang Liu and John T. Abatzoglou and Jennifer C. Adam and Kirti Rajagopalan},

url = {https://www.sciencedirect.com/science/article/pii/S0022169425011710},

doi = {10.1016/j.jhydrol.2025.133833},

issn = {0022-1694},

year  = {2025},

date = {2025-12-01},

urldate = {2025-12-01},

journal = {Journal of Hydrology},

volume = {662},

pages = {133833},

abstract = {While the importance of bivariate precipitation phase partitioning\textemdashthat incorporates both surface air temperature and relative humidity\textemdashhas been established for accurately estimating rain versus snow, hydrology models often rely on a simpler approach that uses only surface-temperature. We evaluate model bias changes for a suite of snow and streamflow metrics between temperature-based rain-snow partitioning (T-RSP) and temperature-relative-humidity-based rain-snow partitioning (TRH-RSP). We used the VIC-CropSyst coupled crop-hydrology model across the Pacific Northwest US as a case study. We found that transition to the TRH-RSP method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (∼50% reduction in mean absolute bias), (b) daily SWE in winter months (reduction of relative bias from −30% to −4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for the majority of the observational snow telemetry stations considered. Depending on the metric, 75\textendash88% of stations showed improvements. Most improvements are in the mid elevation stations. We also find improvements in estimates of basin-level streamflow and the ratio of peak SWE over streamflow. Elevation, temperature exposure, and meteorological bias partly explain the variability in performance improvements across stations. We did see a degradation in bias for snow-off dates. This is likely because meteorological bias and the modeled snowmelt dynamics\textemdashboth of which cannot be resolved by changing the precipitation partitioning\textemdashbecome important in the shoulder months at the end of the cold season. Overall, biases in SWE due to precipitation phase partitioning account for a substantial portion of the overall SWE bias\textemdashat least as much as, if not more than known precipitation biases. Transitioning from T-RSP to TRH-RSP can help us better understand model behavior, improve model accuracies, and better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.},

keywords = {AI, Water},

pubstate = {published},

tppubtype = {article}

}

@article{cravetz_slip_2025b,

title = {Slip detection for compliant robotic hands using inertial signals and deep learning},

author = {Miranda Cravetz and Purva Vyas and Cindy Grimm and Joseph R. Davidson},

url = {https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2025.1698591/full},

doi = {10.3389/frobt.2025.1698591},

issn = {2296-9144},

year  = {2025},

date = {2025-12-01},

urldate = {2025-12-01},

journal = {Frontiers in Robotics and AI},

volume = {12},

publisher = {Frontiers},

abstract = {When a passively compliant hand grasps an object, slip events are often accompanied by flexion or extension of the finger or finger joints. This paper investigates whether a combination of orientation change and slip-induced vibration at the fingertip, as sensed by an inertial measurement unit (IMU), can be used as a slip indicator. Using a tendon-driven hand, which achieves passive compliance through underactuation, we performed 195 manipulation trials involving both slip and non-slip conditions. We then labeled this data automatically using motion-tracking data, and trained a convolutional neural network (CNN) to detect the slip events. Our results show that slip can be successfully detected from IMU data, even in the presence of other disturbances. This remains the case when deploying the trained network on data from a different gripper performing a new manipulation task on a previously unseen object.},

keywords = {AI, Labor},

pubstate = {published},

tppubtype = {article}

}