Identification of coleopteran stored product pest species using discriminant analysis

• Published in: AIP Conference Proceedings
• Main Author: Azis T.M.F.B.; Shariff S.; Mohamad S.; Zulkffle M.A.; Kamaruddin S.A.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179809733&doi=10.1063%2f5.0177088&partnerID=40&md5=57f47ab3d3dc2521225fb5e2dbb4ff72

Coleopteran stored product pests cause significant harm to stored goods. As a result, identifying the insect pest is an important stage in the pest management process. The abundance of insect pest species, on the other hand, may make identification challenging, particularly when utilizing morphological images and molecular approaches. Four morphological measurements of the dorsal part were obtained from 38 Coleopteran stored product pest PaDIL database images. A 100 dataset has been generated based on the body length range. In this paper, the identification of the insect pest species is obtained using Discriminant Analysis (DA). Discriminant function analysis was demonstrated in this paper as a tool for Coleopteran stored product pest identification based on the dataset that has been generated. There is 85% correct classification is obtained from the analysis. © 2023 Author(s).