Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Eoin M. Kenny; Elodie Ruelle; Anne Geoghegan; Laurence Shalloo; Micheál O’Leary; Michael O’Donovan; Mark T. Keane

doi:10.1007/978-3-030-29249-2_12

Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Eoin M. Kenny
, Elodie Ruelle
, Anne Geoghegan
, Laurence Shalloo
, Micheál O’Leary
, Michael O’Donovan
, Mark T. Keane

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

Smart agriculture has emerged as a rich application domain for AI-driven decision support systems (DSS) that support sustainable and responsible agriculture, by improving resource-utilization through better on-farm, management decisions. However, smart agriculture’s promise is often challenged by the high barriers to user adoption. This paper develops a case-based reasoning (CBR) system called PBI-CBR to predict grass growth for dairy farmers, that combines predictive accuracy and explanation capabilities designed to improve user adoption. The system provides post-hoc, personalized explanation-by-example for its predictions, by using explanatory cases from the same farm or county. A key novelty of PBI-CBR is its use of Bayesian methods for case exclusion in this regression domain. Experiments report the tradeoff that occurs between predictive accuracy and explanatory adequacy for different parametric variants of PBI-CBR, and how updating Bayesian priors each year reduces error.

Original language	English
Title of host publication	Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings
Editors	Kerstin Bach, Cindy Marling
Publisher	Springer Verlag
Pages	172-187
Number of pages	16
ISBN (Print)	9783030292485
DOIs	https://doi.org/10.1007/978-3-030-29249-2_12
Publication status	Published - 2019
Externally published	Yes
Event	27th International Conference on Case-Based Reasoning, ICCBR 2019 - Nonnweiler, Germany Duration: 8 Sep 2019 → 12 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11680 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Case-Based Reasoning, ICCBR 2019
Country/Territory	Germany
City	Nonnweiler
Period	8/09/19 → 12/09/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Bayesian analysis
Case exclusion
CBR
Smart agriculture
XAI

Access to Document

10.1007/978-3-030-29249-2_12

Cite this

Kenny, E. M., Ruelle, E., Geoghegan, A., Shalloo, L., O’Leary, M., O’Donovan, M., & Keane, M. T. (2019). Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI). In K. Bach, & C. Marling (Eds.), Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings (pp. 172-187). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11680 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-29249-2_12

Kenny, Eoin M. ; Ruelle, Elodie ; Geoghegan, Anne et al. / Predicting Grass Growth for Sustainable Dairy Farming : A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI). Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings. editor / Kerstin Bach ; Cindy Marling. Springer Verlag, 2019. pp. 172-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2392da1f5b7b4a5cbcb2ca3ab43ea1af,

title = "Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)",

abstract = "Smart agriculture has emerged as a rich application domain for AI-driven decision support systems (DSS) that support sustainable and responsible agriculture, by improving resource-utilization through better on-farm, management decisions. However, smart agriculture{\textquoteright}s promise is often challenged by the high barriers to user adoption. This paper develops a case-based reasoning (CBR) system called PBI-CBR to predict grass growth for dairy farmers, that combines predictive accuracy and explanation capabilities designed to improve user adoption. The system provides post-hoc, personalized explanation-by-example for its predictions, by using explanatory cases from the same farm or county. A key novelty of PBI-CBR is its use of Bayesian methods for case exclusion in this regression domain. Experiments report the tradeoff that occurs between predictive accuracy and explanatory adequacy for different parametric variants of PBI-CBR, and how updating Bayesian priors each year reduces error.",

keywords = "Bayesian analysis, Case exclusion, CBR, Smart agriculture, XAI",

author = "Kenny, \{Eoin M.\} and Elodie Ruelle and Anne Geoghegan and Laurence Shalloo and Miche{\'a}l O{\textquoteright}Leary and Michael O{\textquoteright}Donovan and Keane, \{Mark T.\}",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 27th International Conference on Case-Based Reasoning, ICCBR 2019 ; Conference date: 08-09-2019 Through 12-09-2019",

year = "2019",

doi = "10.1007/978-3-030-29249-2\_12",

language = "English",

isbn = "9783030292485",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "172--187",

editor = "Kerstin Bach and Cindy Marling",

booktitle = "Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings",

address = "Germany",

}

Kenny, EM, Ruelle, E, Geoghegan, A, Shalloo, L, O’Leary, M, O’Donovan, M & Keane, MT 2019, Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI). in K Bach & C Marling (eds), Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11680 LNAI, Springer Verlag, pp. 172-187, 27th International Conference on Case-Based Reasoning, ICCBR 2019, Nonnweiler, Germany, 8/09/19. https://doi.org/10.1007/978-3-030-29249-2_12

Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI). / Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne et al.
Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings. ed. / Kerstin Bach; Cindy Marling. Springer Verlag, 2019. p. 172-187 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11680 LNAI).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

TY - GEN

T1 - Predicting Grass Growth for Sustainable Dairy Farming

T2 - 27th International Conference on Case-Based Reasoning, ICCBR 2019

AU - Kenny, Eoin M.

AU - Ruelle, Elodie

AU - Geoghegan, Anne

AU - Shalloo, Laurence

AU - O’Leary, Micheál

AU - O’Donovan, Michael

AU - Keane, Mark T.

PY - 2019

Y1 - 2019

N2 - Smart agriculture has emerged as a rich application domain for AI-driven decision support systems (DSS) that support sustainable and responsible agriculture, by improving resource-utilization through better on-farm, management decisions. However, smart agriculture’s promise is often challenged by the high barriers to user adoption. This paper develops a case-based reasoning (CBR) system called PBI-CBR to predict grass growth for dairy farmers, that combines predictive accuracy and explanation capabilities designed to improve user adoption. The system provides post-hoc, personalized explanation-by-example for its predictions, by using explanatory cases from the same farm or county. A key novelty of PBI-CBR is its use of Bayesian methods for case exclusion in this regression domain. Experiments report the tradeoff that occurs between predictive accuracy and explanatory adequacy for different parametric variants of PBI-CBR, and how updating Bayesian priors each year reduces error.

AB - Smart agriculture has emerged as a rich application domain for AI-driven decision support systems (DSS) that support sustainable and responsible agriculture, by improving resource-utilization through better on-farm, management decisions. However, smart agriculture’s promise is often challenged by the high barriers to user adoption. This paper develops a case-based reasoning (CBR) system called PBI-CBR to predict grass growth for dairy farmers, that combines predictive accuracy and explanation capabilities designed to improve user adoption. The system provides post-hoc, personalized explanation-by-example for its predictions, by using explanatory cases from the same farm or county. A key novelty of PBI-CBR is its use of Bayesian methods for case exclusion in this regression domain. Experiments report the tradeoff that occurs between predictive accuracy and explanatory adequacy for different parametric variants of PBI-CBR, and how updating Bayesian priors each year reduces error.

KW - Bayesian analysis

KW - Case exclusion

KW - CBR

KW - Smart agriculture

KW - XAI

UR - https://www.scopus.com/pages/publications/85072850578

U2 - 10.1007/978-3-030-29249-2_12

DO - 10.1007/978-3-030-29249-2_12

M3 - Conference proceeding

AN - SCOPUS:85072850578

SN - 9783030292485

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 172

EP - 187

BT - Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings

A2 - Bach, Kerstin

A2 - Marling, Cindy

PB - Springer Verlag

Y2 - 8 September 2019 through 12 September 2019

ER -

Kenny EM, Ruelle E, Geoghegan A, Shalloo L, O’Leary M, O’Donovan M et al. Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI). In Bach K, Marling C, editors, Case-Based Reasoning Research and Development - 27th International Conference, ICCBR 2019, Proceedings. Springer Verlag. 2019. p. 172-187. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29249-2_12

Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI)

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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