Archives of Current Research International

Early identification of pest damage is essential for selecting effective control methods, thereby reducing production costs and minimising crop losses.  In contrast, modern techniques such as remote sensing enable rapid and efficient detection over large areas. Hyperspectral remote sensing provides a non-destructive approach for early detection of crop stress caused by insect pests. The present study aimed to detect infestation of major sucking pests of mulberry like pink mealybug (Maconellicoccus hirsutus Green) spiralling whitefly (Aleurodicus dispersus Russell), and thrips (Pseudodendrothrips mori Niwa) using red edge position (REP) derived from hyperspectral reflectance data. Field experiments were conducted in the mulberry variety V1 at Tamil Nadu Agricultural University, Coimbatore. Spectral reflectance measurements were collected using a GER-1500 spectroradiometer from healthy and pest-infested plants at 15, 30, 45 and 60 days after pruning. The REP values were calculated using the linear interpolation method. The results showed significant shifts in REP towards shorter wavelengths in pest-infested plants compared with healthy plants. Spiralling whitefly (Aleurodicus dispersus Russell) infestation produced the largest REP shift (16.59 nm at 15 DAP), followed by pink mealybug (Maconellicoccus hirsutus Green) (7.42 nm at 60 DAP), while thrips (Pseudodendrothrips mori Niwa) showed comparatively smaller shifts during early infestation stages. These findings demonstrate that red edge position derived from hyperspectral data can effectively detect pest-induced stress in mulberry and may serve as a useful tool for early pest monitoring in sericulture systems. The shift of REP towards shorter wavelengths in damaged plants indicates a reduction in chlorophyll content and photosynthetic efficiency. These findings confirm that REP derived from hyperspectral data can serve as a reliable indicator for early detection of pest-induced stress in mulberry crops.