Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth - Huang, Yiqi; Li, Jie; Yang, Rui; Wang, Fukuan; Li, Yanzhou; Zhang, Shuo; Wan, Fanghao; Qiao, Xi; Qian, Wanqiang (Frontiers in Plant Science, 2021)
Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfill the above requirement. However, when working with raw hyperspectral data, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The raw spectral data of M. micrantha were collected using hyperspectral imaging (HSI) in the spectral range of 450 nm–998 nm. A different combination of prepossessing methods, principal component analysis (for dimensional reduction) and three classifiers were used to analyse the collected images. The results showed that a combination of Savitzky-Golay (SG) smoothing, PCA and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements on specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild.