Spectral response of guava leaves under infestation by <i>Costalimaita ferruginea</i> (coleoptera: Chrysomelidae)

Abstract Guava ( Psidium guajava ) is one of the most important fruit crops in Brazil, being popular due to its availability in all seasons and rich nutritional and medicinal value. However, Costalimaita ferruginea is one of the main pests in guava production and directly interferes with its productivity. Symptoms of attack are often determined by visual observation, which may lead to inadequate characterization of the damage caused by this pest. Areas of guava plants (blocks) and plants with four groups of five leaves per plant (replicates) were considered. This study spectrally characterized the spectral response of guava leaves under infestation by Costalimaita ferruginea (Coleoptera: Chrysomelidae). The spectral signature of the leaves was determined using a spectroradiometer. The spectral signatures showed the guava leaves uninfested, moderately infested (level 1) and severely infested (level 2) by C. ferruginea . The uninfested guava leaves showed lower reflectance in the 500–600‐nm spectral range and higher reflectance in the 750–1000‐nm range. The level 1 infested leaves exhibited moderate reflectance between 500 and 600 nm and lower reflectance between 750 and 1000 nm. In contrast, the level 2 infested leaves had a higher reflectance between 500 and 600 nm and a lower reflectance between 750 and 1000 nm. The uninfested leaves showed lower reflectance in the visible spectrum and higher reflectance in the near‐infrared (NIR) spectrum than level 1 and level 2 infested leaves. It is also noted that as the infestation becomes more severe, the reflectance in the visible increases and the NIR decreases, denoting the typical behaviour of plants under biotic stress. Principal Component Analysis (PCA) and linear regression confirmed the efficacy of hyperspectral reflectance in discriminating the damage levels of C. ferruginea . The data generated in this study can be integrated into a hyperspectral bank for future applications in entomology, damage and pest monitoring.