Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

Abstract

Arthropod pests are among the major problems in soybean production and regular field sampling is required as a basis for decision-making for control. However, traditional sampling methods are laborious and time-consuming. Therefore, our goal is to evaluate hyperspectral remote sensing as a tool to establish reflectance patterns from soybean plants infested by various densities of two species of stinkbugs (Euschistus heros and Diceraeus melacanthus (Hemiptera: Pentatomidae)) and two species of caterpillars (Spodoptera eridania and Chrysodeixis includens (Lepidoptera: Noctuidae)). Bioassays were carried out in greenhouses with potted plants placed in cages with 5 plants infested with 0, 2, 5, and 10 insects. Plants were classified according to their reflectance, based on the acquisition of spectral data before and after infestation, using a hyperspectral push-broom spectral camera. Infestation by stinkbugs did not cause significative differences in the reflectance patterns of infested or non-infested plants. In contrast, caterpillars caused changes in the reflectance patterns, which were classified using a deep-learning approach based on a multilayer perceptron artificial neural network. High accuracies were achieved when the models classified low (0 + 2) or high (5 + 10) infestation and presence or absence of insects. This study provides an initial assessment to apply a non-invasive detection method to monitor caterpillars in soybean before causing economic damage.

Keywords

• <i>Glycine max</i>
• sampling
• pest management
• caterpillars
• stinkbugs