Rice brown planthopper monitoring and detection by spectral reflectance: a review

Abstract

Brown planthopper (BPH) has been one of the main pests of rice worldwide. Monitoring is important factor for determining attacks and estimating their effects. The traditional monitoring approach is usually conducted through visual observation and field scouting, with limitations such as subjectivity and time consumption. Remote sensing is an alternative pest monitoring method that covers a larger area in a shorter time. This paper discusses a remote-sensing method that uses a spectral approach to detect BPH attacks. Literature was filtered and processed using the PRISMA method. According to the spectral sensor, studies were classified into multispectral and hyperspectral sensors. Based on this scale, there are four studies on the panicle, leaf, canopy, and field levels. The model used single-wave reflectance and spectral indices as predictors. Various algorithms were used in the studies: linear regression, Principal Component Analysis, and Machine Learning to estimate the severity class, BPH Population density, and yield loss. A combination of spectral reflectance with other parameters, such as weather, fertilizer application, and infestation time, was conducted to improve the performance of the detection model. This review provides state-of-the-art spectral reflectance usage for detecting BPH attacks and opportunities for future development.