A Method of Invasive Alien Plant Identification Based on Hyperspectral Images

Invasive alien plants (IAPs) are considered to be one of the greatest threats to global biodiversity and ecosystems. Timely and accurate detection technology is needed to identify these invasive plants, helping to mitigate the damage to farmland, fruit trees and woodland. Hyperspectral technology has the potential to identify similar species. However, the challenge remains to simultaneously identify multiple invasive alien plants with similar colors based on image data. The spectral images were collected by a hyperspectral camera with a spectral range of 450-998 nm, and the raw spectra were extracted by Cubert software. First derivative (FD), Savitzky-Golay (SG) smoothing and standard normal variate (SNV) were used to preprocess the raw spectral data, respectively. Then, on the basis of preprocessing, principal component analysis (PCA) and ant colony optimization (ACO) were used for feature dimensionality reduction, and the reduced features were used as input variables for later modeling. Finally, a combination of both dimensionality reduction and non-dimensionality reduction is used for identification using support vector machines (SVM) and random forests (RF). In order to determine the optimal recognition model, a total of 18 combinations of different preprocessing methods, dimensionality reduction methods and classifiers were tested. The results showed that a combination of SG smoothing and SVM achieved a total accuracy (A) of 89.36%, an average accuracy (AA) of 89.39% and an average precision (AP) of 89.54% with a test time of 0.2639 s. In contrast, the combination of SG smoothing, the ACO, and SVM resulted in weaker performance in terms of A (86.76%), AA (86.99%) and AP (87.22%), but with less test time (0.0567 s). The SG-SVM and SG-ACO-SVM models should be selected considering accuracy and time cost, respectively, for recognition of the seven IAPs and background in the wild.