A Method of Invasive Alien Plant Identification Based on Hyperspectral Images

Abstract

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.

Rapid and nondestructive watermelon (Citrullus lanatus) seed viability detection based on visible near‐infrared hyperspectral imaging technology and machine learning algorithms

“…Comparing the accuracy of the model on raw, SNV, and SG spectra data shows an improvement from 7.67% to 12.50%, respectively. The aforementioned analysis process converts the data into an arrangement that can be more quickly and efficiently processed for model performance (Nirere et al., 2022; Qiao et al., 2022). The foundation of all subsequent studies was established based on preprocessed spectra data.…”

“…First, the PCA wavelength selection algorithms were adopted as feasible methods to decrease redundant information and reduce data dimensionality (Qiao et al., 2022). Figure 6b shows the results of the selected three principal components (PCs), for the first PC1 with 86%, the second PC2 with 9%, and the third PC3 with 3%, with an assumption of explaining 98% of the total variance and 2% of residual variance.…”

“…Figure 6c explains the variance of each PC, where the blue color represents the validation and red represents calibration. Even though PCA can present a high total variance, PCA has the drawback of not performing the classification with accurate validations (Qiao et al., 2022).…”

“…The aforementioned analysis process converts the data into an arrangement TA B L E 1 Results analysis of the support vector machine (SVM) model using different data pretreated. that can be more quickly and efficiently processed for model performance (Nirere et al, 2022;Qiao et al, 2022). The foundation of all subsequent studies was established based on preprocessed spectra data.…”

Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows

“…In contrast, RF is a machine learning algorithm based on decision trees and the principle of majority voting; the simple operating principle makes the RF algorithm require less processing time and less computational power in the case of heterogeneous systems [53,54] than other ML algorithms [47,55]. Both RF and SVM algorithms were successfully used to identify, among others, seven herbaceous plants (Mikania micrantha, Sphagneticola calendulacea, Ageratum conyzoides, Mimosa pudica, Lantana camara, Lpomoea cairica, and Bidens pilosa) in China using 138 hyperspectral bands of spectrograph S185 (OA SVM : 89%; OA RF : 84%) [56] and wood small-reed (Calamagrostis epigejos), blackberry (Rubus), and goldenrod (Solidago) in southern Poland using 30 MNF bands of airborne HySpex images (OA SVM : 91%; OA RF : 93%) [57].…”