Precision agriculture has enabled significant progress in improving yield outcomes for farmers. Recent progress in sensing and perception promises to further enhance the use of precision agriculture by allowing the detection of plant diseases and pests. When coupled with robotics methods for spatial localisation, early detection of plant diseases will al- low farmers to respond in a timely and localised manner to dis- ease outbreaks and limit crop damage. This paper proposes the use of hyperspectral imaging (VNIR and SWIR) and machine learning techniques for the detection of the Tomato Spotted Wilt Virus (TSWV) in capsicum plants. Discriminatory features are extracted using the full spectrum, a variety of vegetation indices, and probabilistic topic models. These features are used to train classifiers for discriminating between leaves obtained from healthy and inoculated plants. The results show excellent discrimination based on the full spectrum and comparable results based on data-driven probabilistic topic models and the domain vegetation indices. Additionally our results show increasing classification performance as the dimensionality of the features increase.