Session: 06-11-01: Biotechnology and General Applications

Paper Number: 145421

145421 - Index for Efficient Pest Detection Using Multispectral Imaging 

Pesticides have many benefits for agriculture but do pose a problem when unmanaged. Pests can be difficult to detect and if pest activities such as growth and travel patterns can be easily recorded, then there can be an early detection of any potential problems such as overgrowth that can lead to environmental hazards. Another important benefit of detection methods for pests, is the detection of invasive species that affect agriculture by decimating crops, causing economic losses and disruptions in food supply chains, especially during cross border activities. In this work, multispectral imaging is used to develop an index for pest detection. A selected pest such as beetles is used in different environments. A new index would be able to capture spectral patterns or signatures associated with pest presence/damage with a high accuracy. The intended research can be expanded for many different pest species. The experimental setup is made with four different illumination sources including UV, green, red, and near infrared/IR. Using a multispectral camera, a reflectance analysis can be conducted from the images acquired. Performance and accuracy of the method will be evaluated. Ultimately, this study can aid reducing the risk of invasive pests entering the US borders undetected, which can improve the food supply chains and strengthen the nation’s economy.

Presenting Author: Maurizio Manzo university of north texas

Presenting Author Biography: Dr. Maurizio Manzo is an Associate Professor at the Department of Mechanical Engineering at the University of North Texas (UNT).

Authors:

Maurizio Manzo university of north texas
Carson Melead university of north texas
Josue Arellanes university of north texas