Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method

ABSTRACT

The brown planthopper (BPH) is an important pest in rice. Rice losses due to BPH's damage are often more than 10% of yield. Assessments on loss rates from BPH are now basically dependent on experiential indices. However, early assessments on rice yield losses using hyperspectral data are still rare. In this study, reflectance from rice canopy was measured in net cages after different densities of BPH release. Results showed that reflectance in the near-infrared region (750–1000 nm) from milk grain stage, and in 400–531 nm and 567–705 nm from mature grain stage was closely related to BPH density. These spectral indices: red-edge parameters (λ_r, Dλ_r, Sλ_r), ratio of the maximal reflectance in green (490–560 nm) to minimal reflectance (640–740 nm) in red (R_GREEN/R_RED), ratio of the near-infrared peak (R_NIR) to R_RED, normalized difference vegetation index between R_NIR and R_RED and soil-adjusted vegetation index (SAVI) from both milk and mature grain stages, indicated BPH densities well. As expected, rate of loss in rice grain was higher with increasing BPH density. SAVI, Dλ_r, Sλ_r and reflectance at 760 nm (R₇₆₀) from milk grain stage were significantly correlated with rates of loss in panicle, and 1000-grain weight and multiple-linear regression models for detecting loss rate were established. The hyperspectral reflectance from rice at milk grain stage can be used to assess rice yield losses due to BPH and improve management policies.

KEYWORDS:

• Hyperspectral remote sensing
• reflectance
• rice planthopper
• rice yield
• spectral index
• regression model

Acknowledgments

We thank Bodlah M. A. for language corrections. We also thank two anonymous reviewers and editors for constructive comments on this manuscript.

Disclosure statement

The authors declare no potential conflict of interest.

Additional information

Funding

This work was supported by the Jiangsu Province Agricultural Science and Technology Independent Innovation Fund Project [grant number CX (15) 1055]; National Basic Research Program of China (973 Program) [grant number 2010CB126201].