Early Detection of Aphid (Myzus persicae) Infestation on Chinese Cabbage by Hyperspectral Imaging and Feature Extraction

Abstract

Abstract. Hyperspectral imaging (HSI) in the visible/near-infrared region coupled with textural feature extraction were used to detect aphid infestation on Chinese cabbage plants. Hyperspectral images of Chinese cabbage plants, including 31 healthy plants and 32 aphid-infested plants, were captured in the region of 380 to 1030 nm with a pushbroom HSI system. A total of 160 spectra were selected from the hyperspectral images of plants. Six important wavelengths (IWs) at 550, 671, 694, 742, 911, and 968 nm were identified by x-loadings of PC-1 and PC-2 in principle component analysis (PCA). Textural features (TFs) including contrast, homogeneity, energy, and correlation based on gray-level co-occurrence matrix (GLCM) were extracted from the six selected IWs. Finally, least squares support vector machine (LS-SVM) models were developed to differentiate between healthy and aphid-infested plants based on the IWs and TFs. The results showed that the reflectance and textural features at 694 and 742 nm offered good performance in distinguishing aphid-infested samples from healthy samples. The overall results revealed the feasibility of HSI for early detection of aphid infestation on Chinese cabbage plants and its promising potential in practice. Keywords: Aphid infestation, Chinese cabbage, Hyperspectral imaging, Least squares support vector machine, Principal component analysis, Textural feature extraction.