Rice Brown Planthopper Monitoring and Detection by Spectral Reflectance: A Review Muh Dimas Arifin(a,b*), Yonny Koesmaryono (a), Impron (a)
a)Department of Geophysics & Meteorology, IPB University
Jalan Meranti Wing 19 Level 4 Gedung FMIPA Kampus IPB, Bogor, Jawa Barat 16680 Indonesia
b)East Kalimantan Assessment Institute for Agricultural Technology
Jalan P.M. Noor, Samarinda, Kalimantan Timur 75119 Indonesia
* muh.dimasarifin[at]outlook.com
Abstract
Brown planthopper (BPH) is one of the main pests of rice worldwide. Monitoring is an important thing in determining attack and estimating the effect. The traditional monitoring approach is usually conducted by visual observation and field scouting with limitations: subjectivity and time consumption. Remote sensing is an alternative method to carry out pest monitoring to cover a larger area in a shorter time. This paper discusses one of the remote sensing methods that use the spectral approach in detecting BPH attacks. Literature was filtered and processed by using the PRISMA method. According to the spectral sensor, the studies are classified into multispectral and hyperspectral sensors. Based on the scale, there are four groups of studies on the panicle, leaf, canopy, and field levels. The models^ used single-wave reflectance and spectral indices as the predictor. Various algorithms were used in the studies: Linear regression, Principal Component Analysis, and Machine Learning to estimate severity class, BPH Population density, or yield loss. Various algorithms were used in the studies: Linear regression, Principal Component Analysis, and Machine Learning to estimate the severity class, BPH population density, or yield loss. The 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 the state-of-the-art of spectral reflectance usage in detecting BPH attacks and the opportunity for future development.
