A novel method for detecting brown planthopper (Nilaparvata lugens Stål) early infestation using dual-temporal hyperspectral images

Abstract

Accurate and prompt monitoring of brown planthopper (BPH) infestation is crucial for rice production stability. The unique advantages of remote sensing in mapping the location and severity of pest damage are widely acknowledged. However, the crypticity of BPH early damage complicates the identification of infested areas. This study aims to detect BPH early infestation in paddy fields using an unmanned aerial vehicle (UAV) hyperspectral imaging system. Two data acquisition campaigns were conducted during the BPH early infestation stage. Considering the dynamic spatial distribution of BPH, the pest population density records were averaged to indicate infestation severity during the investigation period. Three novel indices were designed to detect the BPH early damage. Specifically, the Dual-temporal Stressed Canopy Spectral Relative Difference Index (DSRI) and the Dual-temporal Stressed Canopy Spectral Direct Difference Index (DSDI) were proposed based on the dual-temporal spectral changes of rice canopy. Furthermore, an opposite trend of DSDI in the short-wavelength (399–750 nm) and long-wavelength (750–1006 nm) spectral regions was observed for samples with varying BPH severity. Thus, the DSDI-SL was further proposed. The optimal feature combination of DSRIs, DSDIs and DSDI-SLs was selected using Lasso regularization and recursive feature elimination (RFE). An XGBoost classifier was applied to establish the BPH early detection model, which achieved an overall accuracy (OA) of over 85%, outperforming the model established by mono-temporal collected data. In the context of global climate change and escalating challenges to food security, our research introduces a novel framework for the efficient detection and quantitative description of early-stage BPH damage.