Extension
It Starts With Us
Author: Raul Villanueva
Planning Unit: Entomology
Major Program: Horticulture, Commercial
Outcome: Intermediate Outcome
The immature form or larva of the codling moth (CM) is the most devastating global pest of apples with a huge potential impact on the post-harvest quality and yield of the product. Detection is hard due to the small size of its larvae and potentially hidden behavior, simple visual inspection is ill-suited for accurate infestation detection. For two years a multidisciplinary effort involving three UK faculty: Drs. A. Adedeji, K. Donohue and R. Villanueva from three departments (Biosystems and Agricultural Engineering, Electrical and Computer Engineering, and Entomology) studies were conducted to detect vibro-acoustic signals of multiple behaviors of CM larvae (chewing and boring). Then, two different approaches were proposed to build on this previous work: multi-domain feature extraction with machine learning to show basic classification potential, and matched filter-aided classification to show the effects of preprocessing using the larval behavior templates with an additional low-intensity heat stimulation to improve larvae’s hidden activity rate. The findings of his study suggest that the vibro-acoustic technique can be an adaptable tool for detecting CM infestation in apples and improve post-harvest classification quality in fruit. This study was funded by a NIFA grant and two peer-reviewed manuscripts were already published, and funding are used to support an student in the Biosystems and Agricultural Engineering Dept. that is completing his PhD
