Journal of Experimental Agriculture International

Thrips (Pseudodentrothrips mori) infestation is an emerging constraint in mulberry cultivation, causing subtle yet significant deterioration in leaf quality that adversely affects silkworm growth and cocoon productivity. Early detection of thrips damage using conventional field scouting is difficult due to the inconspicuous nature of initial symptoms. The present study evaluated the potential of hyperspectral radiometry for non-destructive detection and quantification of thrips-induced stress in mulberry (Morus spp.). The present study evaluated the potential of hyperspectral radiometry for detection, discrimination, and quantification of thrips-induced stress in mulberry through band sensitivity analysis, vegetation index sensitivity analysis, regression analysis and linear correlation intensity analysis. Field experiments were conducted in a V1 mulberry garden using naturally infested and protected control plots in Tamil Nadu Agricultural University, Coimbatore during 2018. Spectral reflectance data were collected using a field-portable hyperspectral spectroradiometer (350–1050 nm) at different crop stages, and vegetation indices including NDVI, Simple Ratio (SR), and GRVI were derived. Thrips infestation resulted in increased reflectance in the visible region and reduced reflectance in the near-infrared region, indicating chlorophyll degradation and internal tissue disruption. Vegetation indices were consistently lower in damaged plants compared to healthy plants, with SR and NDVI showing higher sensitivity to thrips-induced stress. Linear regression analysis revealed strong negative relationships between percent leaf damage and NDVI (R² = 0.886) and SR (R² = 0.791), whereas GRVI exhibited poor predictive capability. Linear correlation intensity analysis identified diagnostically important wavelengths, with the highest positive correlation observed at 689.45 nm (r = 0.94) in the red region and the strongest negative correlation at 763.26 nm (r = −0.10) in the NIR region. Overall, the study demonstrates that hyperspectral radiometry combined with correlation intensity analysis and vegetation indices provides a robust, non-invasive approach for early detection and assessment of thrips damage in mulberry, supporting precision pest management in sericulture systems.