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Innovative use of machine learning to forecast crop disease risk

Innovative use of machine learning to forecast crop disease risk

Potatoes in a field next to the FindOUT app logo
“There is added value in combining the algorithms in an ensemble to provide a more accurate and robust forecasting tool that can be tailored to produce region-specific alerts. The techniques used can easily be applied to outbreak data from other crop diseases to derive tools to help farmers and land managers make the best decisions.”
Potato beds in a field (Image by Wolfgang Ehrecke from Pixabay)

Crop diseases can generate destructive outbreaks that have the potential to threaten global food security, which is why it is fundamental to have reliable data promptly available from disease surveillance programs and outbreak investigations. In many cases, however, only information on outbreaks is collected and data from surrounding healthy crops is omitted. Use of such data to develop models that can forecast risk/no-risk of disease is therefore problematic, as information relating to the no-risk status of healthy crops is missing.

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Bernardo Rodriguez-Salcedo, Media Manager, James Hutton Institute, Tel: +44 (0)1224 395089 (direct line), +44 (0)344 928 5428 (switchboard) or +44 (0)7791 193918 (mobile).

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This article was originally posted by The James Hutton Institute