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.
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Notes
- 1.
Several other approaches such as linear regression, neural networks, SVMs and tree algorithms were also tested alongside this CBR system. The CBR system’s accuracy equalled or bettered these other systems.
- 2.
Predictions could only be made for 2017 because the earlier years of the PBI dataset (2013–2016) have too few cases, as the DSS was in its early years of adoption.
- 3.
The relatively large value of 4 was chosen to represent that we are not highly certain of the validity of the gold standard prior mean when compared to a typical dairy farm pasture.
- 4.
The variance \( \sigma^{2} \) wasn’t adapted; if it changes it could lead to an unfair evaluation as updated-variants may differ a lot in the amount of data excluded compared to non-updated variants.
- 5.
Note, for transform methods some knowledge about a given week’s data distribution would need to be inferred if we were doing this in a live-system for formula (2) to be used.
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Acknowledgements
This publication has emanated from research conducted with the financial support of (i) Science Foundation Ireland (SFI) to the Insight Centre for Data Analytics under Grant Number 12/RC/2289 and (ii) SFI and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland to the VistaMilk SFI Research Centre under Grant Number 16/RC/3835.
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Kenny, E.M. et al. (2019). 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. (eds) Case-Based Reasoning Research and Development. ICCBR 2019. Lecture Notes in Computer Science(), vol 11680. Springer, Cham. https://doi.org/10.1007/978-3-030-29249-2_12
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