Predicting Potato Late Blight Disease Pressure using Artificial Neural Network
Abstract
Late blight causing Phytopthora infestans (Mont.) de Bary largely depends on weather
parameters such as temperature and relative humidity for survival, spread and its
ability to attach and infect new plants. The variations in weather across different
agro-ecological zones can be used to explain the different levels of disease severity
experienced across these regions.
Potato late blight disease evaluation data from five locations was coupled with GISlinked
weather data. A series of neural network models were developed and validated
with 10-fold cross validation and the optimal model selected based on accuracy
achieved on validation set. The selected model had 1 hidden layer with 14 nodes
achieving an accuracy of 88% in the validation set. The final model was used to predict
disease severity with 89% accuracy on new data. It was also found that the number
of precipitation days and number of days with temperature and relative humidity
favorable to disease development were amongst the top significant variables in the
model hence a target for monitoring.
This model can be used to estimate the expected late blight severity in a target region
hence support the decisions on the appropriate varieties and management regimes to
be used, reducing yield loss and excessive use of fungicides.
Publisher
University of Nairobi
Subject
Potato Late Blight DiseaseRights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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