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
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After two years of interruption two events that take place were completed at the Nature center at La... Read More
Immediately after the tornado that destroyed all the facilities of the Research and Education Center... Read More