As the quantity and quality of high resolution data from remote sensing, meteorological monitoring and large scale field studies increase, plant protection scientists face a growing need for efficient data analysis methods. As computational power improves, scientists are turning to emerging statistical modelling methods to analyse information that can challenge more classical statistical analysis methods. Additionally, a greater recognition of the complexity of interactions of pests and diseases with the heterogeneous environment requires new approaches to answer questions about management of plant pests and diseases, including not only those established in New Zealand but also those that present a biosecurity threat. New approaches are required especially when detailed functional relationships between species and environment are not known. We highlight a range of research applying computational intelligence and machine learning methods such as artificial neural networks and emergent statistical modelling techniques for prediction and knowledge discovery in plant protection.