Description: The invasive plant known as bugweed (Solanum mauritianum) is a notorious invader of forestry plantations in the eastern parts of South Africa. Not only is bugweed considered to be one of five most widespread invasive alien plant (IAP) species in the summer rainfall regions of South Africa but it is also one of the worst invasive alien plants in Africa. It forms dense infestations that not only impacts upon commercial forestry activities but also causes significant ecological and environment damage within natural areas. Effective weed management efforts therefore require robust approaches to accurately detect; map and monitor weed distribution in order to mitigate the impact on forestry operations. The main objective of this research was to determine the utility of support vector machines (SVMs) with a 272-waveband AISA Eagle image to detect and map the presence of co-occurring bugweed within mature Pinus patula compartments in KwaZulu Natal. The SVM when utilized with a recursive feature elimination (SVM-RFE) approach required only 17 optimal wavebands from the original image to produce a classification accuracy of 93% and True Skills Statistic of 0.83. Results from this study indicate that (1) there is definite potential for using SVMs for the accurate detection and mapping of bugweed in commercial plantations and (2) it is not necessary to use the entire 272-waveband dataset because the SVM-RFE approach identified an optimal subset of wavebands for weed detection thus enabling improved data processing and analysis.