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The application of artificial neural networks in plant protection *
Author(s) -
Peacock L.,
Worner S.,
Pitt J.
Publication year - 2007
Publication title -
eppo bulletin
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.327
H-Index - 36
eISSN - 1365-2338
pISSN - 0250-8052
DOI - 10.1111/j.1365-2338.2007.01123.x
Subject(s) - artificial neural network , bootstrapping (finance) , logistic regression , biosecurity , artificial intelligence , computer science , machine learning , ecology , mathematics , biology , econometrics
Artificial neural networks are powerful predictive tools that have the ability to detect and approximate non‐linear relationships from the data. In an explorative analysis, artificial neural networks were used to predict the geographic distribution of groups of polyphagous plant pests. Using climate variables as predictors, artificial neural network models were compared with binary logistic models for predicting insect distribution. Using bootstrapping, artificial neural networks were shown to predict insect presence and absence significantly better than the binary logistic regression models. Results from the study suggest that artificial neural networks have the potential for application in many areas of plant protection and biosecurity.