A two-step approach to mitigating phenological influences on spectroscopic detection of rice blast and removing pseudo severity estimates in healthy plants

Rice blast (RB) is a global fungal threat that occurs over multiple growth stages. The spatially explicit mapping of RB severity with remote sensing is crucial for precision crop protection. However, the interactions between spectral variations caused by the pathogen infection and phenological growth remain poorly understood in disease monitoring. This interference prevents the successful mapping of disease dynamics by introducing substantial errors in areas dominated by healthy plants. This study aimed to reveal the phenological influence on RB detection and to determine key spectral indicators for accurate classification for eliminating the interference of healthy plants on severity assessments. To achieve this goal, experimental data across growth stages were collected and comprised ground truth evaluations to derive the disease index (DI), and ground-based canopy reflectance and hyperspectral images acquired by an unpiloted aerial system (UAS). These datasets were used to examine the spectral responses to both i) RB infection, and ii) phenology, assessing the false positives obtained in the RB detection. Moreover, a two-step method was applied including the RB detection based on a novel feature selection method termed sequential importance selection (SIS) and the DI estimation using linear models based on rice blast indices (RIBIs). The results demonstrated that the spectral signatures in responses to phenology and RB infection were highly similar in the red and near-infrared regions, as well as in traditional vegetation indices (VIs) associated with plant traits. Such similarity yielded considerable false positive rates (FPR) in RB detection and pseudo-DI in healthy plots when applying individual VIs. In RB detection, the VIs selected by SIS (VI_SIS) achieved significantly higher overall accuracy (OA) and lower FPR than the best RIBI variant across multiple phenological phases (VI_SIS: OA = 92 %, F1 = 0.93, FPR = 0.13; aRIBI_nir: OA = 70.7 %, F1 = 0.75, FPR = 0.36). Moreover, the proposed two-step approach eliminated false positives and wrong DI estimation effectively in healthy plants. Our findings suggested the potential of feature selection in overcoming the phenological influence in RB detection, as well as the necessity of disease detection before severity quantification in eliminating the pseudo severity estimates in healthy plants.