The use of data mining to assist crop protection decisions on kiwifruit in New Zealand
A method is developed to predict insecticide spray decisions using machine learning. Spray diary data are used to predict the outcome of spray monitoring decisions. Using a naive Bayes model 70% of no-spray decisions were made to accuracy of 95%. The method provides new insights into factors affecting pest incidence and control. The method has wide application if linked with on-orchard data capture measures.
data mining machine learning pest management decision support