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

Author(s)

Iost Filho, Fernando Henrique ; Pazini, Juliano de Bastos ; de Medeiros, Andre Dantas ; Rosalen, David Luciano ; Yamamoto, Pedro Takao

• Document type: Journal article
• Source: AGRONOMY-BASEL; v. 12, n. 7, p. 20-pg., 2022-07-01.

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. (AU)

• FAPESP's process: 19/26099-0 - Multi and hyperspectral imaging applied in discrimination and estimation of the arthropod pests infestation level on soybean
• Grantee: Juliano de Bastos Pazini
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/02317-5 - Center for Excellence in Biological Control
• Grantee: José Roberto Postali Parra
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• FAPESP's process: 17/19407-4 - Remote sensing techniques for monitoring arthropod pests in agricultural fields
• Grantee: Pedro Takao Yamamoto
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 19/26145-1 - Monitoring insect pests in soybean fields using remote sensing
• Grantee: Fernando Henrique Iost Filho
• Support Opportunities: Scholarships in Brazil - Doctorate