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Related Experiment Video

Updated: Jun 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Decision aids for multiple-decision disease management as affected by weather input errors.

W F Pfender1, D H Gent, W F Mahaffee

  • 1U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Forage Seed and Cereal Research Unit, 3450 SW Campus Way, Corvallis, OR 97331, USA. pfenderw@onid.orst.edu

Phytopathology
|November 25, 2010
PubMed
Summary

Errors in weather data used by disease management decision support systems (DSSs) can impact recommendations. An error analysis comparing DSS outcomes with accurate versus flawed weather data is crucial for assessing management decision quality.

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Last Updated: Jun 6, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Agricultural Science
  • Plant Pathology
  • Data Science

Background:

  • Disease management decision support systems (DSSs) often use weather data to predict disease hazards.
  • Inaccurate weather inputs (forecasting, interpolation, off-site data) can compromise DSS calculations and management advice.
  • The impact of weather input errors on management outcomes varies with decision complexity, from single choices to season-long processes.

Purpose of the Study:

  • To investigate the impact of weather input errors on the accuracy of disease management decision support systems (DSSs).
  • To evaluate how different types of DSSs and decision-making contexts influence the effect of weather data inaccuracies.
  • To propose and illustrate an error analysis method for quantifying the uncertainty in DSS outcomes due to weather data quality.

Main Methods:

  • Conducted an error analysis by comparing DSS outcomes generated with high-quality weather data against those using weather data with introduced bias and/or variance.
  • Applied the analytical approach to two distinct DSS types: an infection risk index for hop powdery mildew and a simulation model for grass stem rust.
  • Examined the challenges in quantifying accuracy for multi-decision DSSs, considering factors like overlapping disease events and adaptive management strategies.

Main Results:

  • Weather input errors can significantly affect disease hazard indicators and subsequent management recommendations from DSSs.
  • The sensitivity of DSS outcomes to weather input errors depends on the system's complexity and the nature of the disease management decisions.
  • The proposed error analysis method provides a framework for assessing the impact of weather data quality on DSS performance.

Conclusions:

  • Accurate weather data is critical for reliable disease management decision support.
  • Further research into analytical methods is needed to effectively address uncertainty quantification in complex, multi-decision DSSs.
  • Understanding the impact of weather input errors is essential for improving the robustness and trustworthiness of agricultural DSSs.