Identification of Early Peach Aphid Infestation Based on Hyperspectral Imaging Technology

Abstract

Peach aphid is a common pest and hard to detect. This study employs hyperspectral imaging technology to identify early damage in green cabbage caused by peach aphid. Through principal component transformation and multiple linear regression analysis, the correlation relation between spectral characteristics and infestation stage is analyzed. Then, four characteristic wavelength selection methods are compared and optimal characteristic wavelengths subset is determined to be input for modelling. One linear algorithm and two nonlinear modelling algorithms are compared. Finally, support vector machine (SVM) model based on the characteristic wavelengths selected by multi-cluster feature selection (MCFS) acquires the highest identification accuracy, which is 98.97%. These results indicate that hyperspectral imaging technology have the ability to identify early peach aphid infestation stages on green cabbages.

Keywords

peach aphid / hyperspectral imaging / machine learning / green cabbage