2026
1.
Kristen Goebel; William Solow; Paola Pesantez-Cabrera; Markus Keller; Alan Fern
Budgeted Online Active Learning with Expert Advice and Episodic Priors Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 45, pp. 38496–38504, 2026, ISSN: 2374-3468.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@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}
}
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.
2.
Taylor Dinkins; Weng-Keen Wong; Basavaraj Amogi; Paola Pesantez-Cabrera; Jaitun Patel; Lav Khot; Alan Fern
Localized Near Surface Temperature Inversion Forecasting Using Long Short-Term Memory Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 47, pp. 40249–40257, 2026, ISSN: 2374-3468.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@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}
}
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.
2025
3.
Nathan Balcarcel; Paola Pesantez-Cabrera; Kristen Goebel; Markus Keller; Lav Khot; Alan Fern; Ananth Kalyanaraman
PhenoTracker: A machine learning model to track grape phenology Proceedings Article
In: Workshop Proceedings of the 54th International Conference on Parallel Processing, pp. 104–111, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 979-8-4007-2109-0.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@inproceedings{balcarcel_phenotracker_2025,
title = {PhenoTracker: A machine learning model to track grape phenology},
author = {Nathan Balcarcel and Paola Pesantez-Cabrera and Kristen Goebel and Markus Keller and Lav Khot and Alan Fern and Ananth Kalyanaraman},
url = {https://dl.acm.org/doi/10.1145/3750720.3758079},
doi = {10.1145/3750720.3758079},
isbn = {979-8-4007-2109-0},
year = {2025},
date = {2025-12-01},
urldate = {2025-12-01},
booktitle = {Workshop Proceedings of the 54th International Conference on Parallel Processing},
pages = {104\textendash111},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {ICPP Workshops \'25},
abstract = {Accurate forecasting of crop phenology supports farm management decisions and mitigation strategies to prevent crop loss. In grapevines, phenological development involves complex, cultivar-specific responses to environmental conditions, making prediction challenging. Traditional process-based models rely primarily on growing degree days (GDD) derived from air temperature, overlooking other influential factors. In this work, we leverage expanded weather data inputs (i.e., air temperature, relative humidity, dew point, precipitation, and wind speed) and machine learning to model grape phenology. Using a 20-year dataset spanning 20 grape cultivars, we train a recurrent neural network to forecast phenological progression. Our model outperforms GDD-based baselines in predicting budbreak, bloom, and veraison growth stages with respective root mean squared error in the ranges of 4.98-8.61 days, 1.22-4.80 days, and 2.24-4.38 days for four major grapevine cultivars. Model also provides confidence intervals for its forecasts.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {inproceedings}
}
Accurate forecasting of crop phenology supports farm management decisions and mitigation strategies to prevent crop loss. In grapevines, phenological development involves complex, cultivar-specific responses to environmental conditions, making prediction challenging. Traditional process-based models rely primarily on growing degree days (GDD) derived from air temperature, overlooking other influential factors. In this work, we leverage expanded weather data inputs (i.e., air temperature, relative humidity, dew point, precipitation, and wind speed) and machine learning to model grape phenology. Using a 20-year dataset spanning 20 grape cultivars, we train a recurrent neural network to forecast phenological progression. Our model outperforms GDD-based baselines in predicting budbreak, bloom, and veraison growth stages with respective root mean squared error in the ranges of 4.98-8.61 days, 1.22-4.80 days, and 2.24-4.38 days for four major grapevine cultivars. Model also provides confidence intervals for its forecasts.
4.
Basavaraj R. Amogi; Lav R. Khot; Bernardita V. Sallato
Impact of summer heat and mitigation strategies on apple (Cosmic Crisp®) fruit color dynamics quantified using crop physiology sensing system Journal Article
In: Journal of Agriculture and Food Research, vol. 23, pp. 102163, 2025, ISSN: 2666-1543.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@article{amogi_impact_2025,
title = {Impact of summer heat and mitigation strategies on apple (Cosmic Crisp®) fruit color dynamics quantified using crop physiology sensing system},
author = {Basavaraj R. Amogi and Lav R. Khot and Bernardita V. Sallato},
url = {https://www.sciencedirect.com/science/article/pii/S2666154325005344},
doi = {10.1016/j.jafr.2025.102163},
issn = {2666-1543},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
journal = {Journal of Agriculture and Food Research},
volume = {23},
pages = {102163},
abstract = {Frequent summer heat waves significantly challenge global fruit production, including apples (Malus domestica Borkh.) grown in Washington State, USA. While growers employ heat mitigation strategies like evaporative cooling with overhead sprinklers, foggers, shade/drape netting, and protective sprays, these techniques can inadvertently compromise fruit coloration, a key quality attribute influencing harvest timing, marketability, and consumer acceptance. Thus, this study investigated whether continuous, in-orchard monitoring of fruit color and microclimatic conditions could help optimize mitigation practices without compromising fruit quality. Using a Crop Physiology Sensing System (CPSS), apple (Cosmic Crisp®) fruit color progression and ambient weather conditions were monitored at 5-min intervals throughout the 2022 growing season under fogging, netting, and untreated control treatments. CPSS with integrated RGB imaging data were contrasted with ambient air temperature (Tair) within each treatment using a custom developed algorithm. The algorithm allowed automated and daily quantification of fruit color metrics, including hue angle (h°), color transition from green to red (a∗), and chroma. Results suggest that prolonged daytime Tair exceeding 35 °C could cause significant degradation of red pigmentation (increasing h° and declining a∗). Netting caused overnight heat retention and delayed color recovery, whereas fogging effectively moderated the microclimate, preserving red coloration. Crucially, a nighttime drop in Tair to approximately 12 °C facilitated the reappearance of red coloration. To our knowledge, this is the first study to document both the degradation and subsequent reappearance of apple fruit coloration under field conditions. These findings suggest that continuous apple fruit color and ambient air temperature monitoring could be useful to effectively employ heat mitigation techniques, thereby improving fruit quality and market value at harvest.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {article}
}
Frequent summer heat waves significantly challenge global fruit production, including apples (Malus domestica Borkh.) grown in Washington State, USA. While growers employ heat mitigation strategies like evaporative cooling with overhead sprinklers, foggers, shade/drape netting, and protective sprays, these techniques can inadvertently compromise fruit coloration, a key quality attribute influencing harvest timing, marketability, and consumer acceptance. Thus, this study investigated whether continuous, in-orchard monitoring of fruit color and microclimatic conditions could help optimize mitigation practices without compromising fruit quality. Using a Crop Physiology Sensing System (CPSS), apple (Cosmic Crisp®) fruit color progression and ambient weather conditions were monitored at 5-min intervals throughout the 2022 growing season under fogging, netting, and untreated control treatments. CPSS with integrated RGB imaging data were contrasted with ambient air temperature (Tair) within each treatment using a custom developed algorithm. The algorithm allowed automated and daily quantification of fruit color metrics, including hue angle (h°), color transition from green to red (a∗), and chroma. Results suggest that prolonged daytime Tair exceeding 35 °C could cause significant degradation of red pigmentation (increasing h° and declining a∗). Netting caused overnight heat retention and delayed color recovery, whereas fogging effectively moderated the microclimate, preserving red coloration. Crucially, a nighttime drop in Tair to approximately 12 °C facilitated the reappearance of red coloration. To our knowledge, this is the first study to document both the degradation and subsequent reappearance of apple fruit coloration under field conditions. These findings suggest that continuous apple fruit color and ambient air temperature monitoring could be useful to effectively employ heat mitigation techniques, thereby improving fruit quality and market value at harvest.
5.
Dattatray G. Bhalekar; Srikanth Gorthi; Lav R. Khot; Markus Keller
Real-time Canopy Temperature-driven Automated Fogging for Heat Stress Mitigation in Vineyards Proceedings Article
In: 2025 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 272–276, 2025.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@inproceedings{bhalekar_real-time_2025,
title = {Real-time Canopy Temperature-driven Automated Fogging for Heat Stress Mitigation in Vineyards},
author = {Dattatray G. Bhalekar and Srikanth Gorthi and Lav R. Khot and Markus Keller},
url = {https://ieeexplore.ieee.org/document/11512642},
doi = {10.1109/MetroAgriFor66923.2025.11512642},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {2025 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
pages = {272\textendash276},
abstract = {This study explored the effectiveness of an automated under-canopy fogging-based heat stress mitigation technique. The fogging actuation system was driven by real-time canopy temperature (Tc) monitoring using a crop physiology sensing system (CPSS). Canopy Tc and wetness were continuously monitored using CPSS integrated with thermal-RGB imager and leaf wetness sensor, respectively. The solenoid valves on the pressurized cooling line were interfaced with CPSS for automated fogging actuation at Tc \> 35 °C and wetness \< 0.45. Air (Ta), berry (Tb), soil (Ts), and canopy temperatures were monitored to quantify the effectiveness of automated fogging in contrast with no cooling (control) treatment. At harvest, vine yield, berry weight, total soluble solids, titratable acidity, and pH were measured. Statistical analysis revealed significantly higher Ta, Tb, Tc, and Ts, in control treatment compared to automated fogging during the peak heat hours of the hottest four days of the 2023 season. There were no significant differences in yield and berry composition, except for pH, in automated fogging and control treatment. This trend could be attributed to unreliable CPSS performance in edge compute and fogging actuation during extreme heat events. Overall, although CPSS has demonstrated cooling automation potential, further improvements are needed for robust operations.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {inproceedings}
}
This study explored the effectiveness of an automated under-canopy fogging-based heat stress mitigation technique. The fogging actuation system was driven by real-time canopy temperature (Tc) monitoring using a crop physiology sensing system (CPSS). Canopy Tc and wetness were continuously monitored using CPSS integrated with thermal-RGB imager and leaf wetness sensor, respectively. The solenoid valves on the pressurized cooling line were interfaced with CPSS for automated fogging actuation at Tc > 35 °C and wetness < 0.45. Air (Ta), berry (Tb), soil (Ts), and canopy temperatures were monitored to quantify the effectiveness of automated fogging in contrast with no cooling (control) treatment. At harvest, vine yield, berry weight, total soluble solids, titratable acidity, and pH were measured. Statistical analysis revealed significantly higher Ta, Tb, Tc, and Ts, in control treatment compared to automated fogging during the peak heat hours of the hottest four days of the 2023 season. There were no significant differences in yield and berry composition, except for pH, in automated fogging and control treatment. This trend could be attributed to unreliable CPSS performance in edge compute and fogging actuation during extreme heat events. Overall, although CPSS has demonstrated cooling automation potential, further improvements are needed for robust operations.
2024
6.
Basavaraj R. Amogi; Lav R. Khot; Bernardita V. Sallato
Localized Crop Physiology Sensing System Driven Apple Fruit Color Progression Monitoring Proceedings Article
In: 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 232–236, 2024.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@inproceedings{amogi_localized_2024b,
title = {Localized Crop Physiology Sensing System Driven Apple Fruit Color Progression Monitoring},
author = {Basavaraj R. Amogi and Lav R. Khot and Bernardita V. Sallato},
url = {https://ieeexplore.ieee.org/document/10948846},
doi = {10.1109/MetroAgriFor63043.2024.10948846},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
booktitle = {2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
pages = {232\textendash236},
abstract = {Fruit color is a critical quality attribute that significantly affects the commercial value of apples. Elevated air temperatures and solar radiation during heat waves can substantially impact fruit coloration, while mitigation techniques such as netting can further compromise fruit color development by trapping heat. Continuous monitoring of fruit color throughout the growing season, particularly under heat stress conditions, is thus essential for informed grower decision-making. This study leverages a previously developed localized crop physiology sensing system (CPSS) to enable real time monitoring of apple fruit color progression. Using visible imagery captured by the CPSS, apple fruit color was quantified by extracting the hue angle (ˆtextbackslashcirctextbackslashmathbfh). The results showed that, the influence of sunlight on measured color accuracy (ΔE) is lower and more stable during mid-day hours (1000 h − 1300 h), whereas notable variations were observed during early morning and late afternoon periods. The ˆtextbackslashcirctextbackslashmathbfh hence calculated for RGB images captured around 1200 h was utilized to track fruit color progression. Its monitoring over the summer 2022 growing season showed variations as an effect of environmental stressors, especially in response to heat wave. The developed approach offers a tool for growers to adjust different heat mitigation techniques during heat wave/events to mitigate any negative implication on fruit coloration.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {inproceedings}
}
Fruit color is a critical quality attribute that significantly affects the commercial value of apples. Elevated air temperatures and solar radiation during heat waves can substantially impact fruit coloration, while mitigation techniques such as netting can further compromise fruit color development by trapping heat. Continuous monitoring of fruit color throughout the growing season, particularly under heat stress conditions, is thus essential for informed grower decision-making. This study leverages a previously developed localized crop physiology sensing system (CPSS) to enable real time monitoring of apple fruit color progression. Using visible imagery captured by the CPSS, apple fruit color was quantified by extracting the hue angle (ˆtextbackslashcirctextbackslashmathbfh). The results showed that, the influence of sunlight on measured color accuracy (ΔE) is lower and more stable during mid-day hours (1000 h − 1300 h), whereas notable variations were observed during early morning and late afternoon periods. The ˆtextbackslashcirctextbackslashmathbfh hence calculated for RGB images captured around 1200 h was utilized to track fruit color progression. Its monitoring over the summer 2022 growing season showed variations as an effect of environmental stressors, especially in response to heat wave. The developed approach offers a tool for growers to adjust different heat mitigation techniques during heat wave/events to mitigate any negative implication on fruit coloration.
7.
Basavaraj R. Amogi; Nisit Pukrongta; Lav R. Khot; Bernardita V. Sallato
Edge compute algorithm enabled localized crop physiology sensing system for apple (textitMalus domestica Borkh.) crop water stress monitoring Journal Article
In: Computers and Electronics in Agriculture, vol. 224, pp. 109137, 2024, ISSN: 0168-1699.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@article{amogi_edge_2024,
title = {Edge compute algorithm enabled localized crop physiology sensing system for apple (textitMalus domestica Borkh.) crop water stress monitoring},
author = {Basavaraj R. Amogi and Nisit Pukrongta and Lav R. Khot and Bernardita V. Sallato},
url = {https://www.sciencedirect.com/science/article/pii/S0168169924005283},
doi = {10.1016/j.compag.2024.109137},
issn = {0168-1699},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
journal = {Computers and Electronics in Agriculture},
volume = {224},
pages = {109137},
abstract = {Elevated air temperature (\>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p \< 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {article}
}
Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.
8.
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
9.
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
10.
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.
