Journal of Scientific Research and Reports

Mulberry (Morus spp.) serves as the primary food source for silkworms (Bombyx mori L.). Sucking pests form a major group of destructive fauna affecting mulberry crops. Among these, sap-sucking pests are the most destructive, causing greater losses to mulberry compared to other pest groups. 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. The study aims to perform spectral analysis of spiralling whitefly-damaged Mulberry plants using hyperspectral radiometry. A randomised block design was employed to analyse the vegetative indices, namely RVI, NDVI, and GRVI, with means compared using the Least Significant Difference method. Furthermore, linear regression models were constructed to assess the relationship between the percentage of damage and each of the vegetation indices. Linear correlation intensity analysis was conducted to identify the wavelengths with the highest positive and negative correlation with spiralling white fly damage. Hyperspectral radiometry, which analyses plant spectral reflectance, is particularly effective in assessing pest-induced stress. In the experiments, the mulberry variety V1, known for its different crop duration and susceptibility to insect pests, was chosen. The damage caused by spiralling whitefly (Aleurodicus disperses) was studied. The percentage of damage was recorded in both healthy and infested plots at 15-day intervals during the active damage stage. Spectral reflectance across various bands was also recorded using a hyperspectral radiometer. Sensitivity analysis of spectral bands and vegetation indices (VIs) was carried out. Different analyses, including correlation and regression of pest damage with VIs, and correlation intensity curve analysis, were conducted in order to detect and discriminate individual pest damage and to estimate the extent of damage caused by this pest. These studies were carried out at the mulberry garden of the Department of Sericulture, Tamil Nadu Agricultural University (TNAU), Coimbatore, during 2018.

The results revealed that the spectral reflectance curves of mulberry plants damaged by spiralling whitefly differed from those of healthy plants. In general, damaged plants showed a decrease in near-infrared (NIR) reflectance (770–860 nm), along with an increase in green (520–590 nm) and red reflectance (620–680 nm). The mean values of NDVI, GRVI, and RVI for damaged plants were significantly lower than those of healthy plants. For spiralling whitefly damage, the red band was found to be more sensitive than other bands. Among the vegetation indices, GRVI was most sensitive to spiralling whitefly damage. A significant negative correlation was observed between spiralling whitefly damage and NDVI and GRVI across all observations. The R² values of RVI, NDVI, and GRVI with spiralling whitefly damage were significant, indicating the capability of these indices to estimate damage. To further quantify damage, linear regression equations were developed based on spectral indices. Correlation analysis revealed the highest positive correlation (r = 0.62) in the NIR band at 743 nm, while the least negative correlation (r = –0.40) occurred in the red band at 676.75 nm. The combined use of red-band reflectance and GRVI provides a reliable, non-destructive tool for early detection and monitoring, enabling timely pest management in sericulture.