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This chapter presents the initial studies on the detection of two common diseases and pests, the powdery mildew and spider mites, on greenhouse tomato plants by measuring the chemical volatiles emitted from the tomato plants as the disease develops using a Field Asymmetric Ion Mobility Spectrometry (FAIMS) device. The processing on the collected FAIMS measurements using PCA shows that clear increment patterns can be observed on all the experimental plants representing the gradual development of the diseases. Optimisation on the number of dispersion voltages to be used in the FAIMS device shows that reducing the number of dispersion voltages by a factor up to 10, preserves the key development patterns perfectly, though the amplitudes of the new patterns are reduced significantly.

In this chapter, the authors introduce the principles of some of the most widely used supervised and unsupervised Pattern Recognition (PR) techniques and assess behaviour and performances. A dataset acquired from a set of experiments conducted at University of Warwick is employed to construct a case study in which the techniques will be applied. The chapter will also evaluate the integration of PR methods with an Electronic Nose (EN) device to develop and implement a plant diagnosis tool based on discriminating the Organic Volatile Compounds (VOC) released by plants when attacked by pest. The chapter concludes with a performance comparison and a brief discussion of how an appropriate PR technique can be coupled with an EN to produce a greenhouse plant pest and disease diagnosis system for day-to-day utilisation. Some consideration of further work is also presented.