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. Due to the small size of its larvae and potentially hidden behavior, simple visual inspection is ill-suited for accurate infestation detection. The characteristic vibro-acoustic signals of multiple behaviors of CM larvae such as chewing and boring were identified in a previous study. In this study, 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. Additionally, low-intensity heat stimulation was applied to improve classification results by increasing the larvae's hidden activity rate. The results indicated that the first approach led to accuracies as high as 97.47 % for an acoustic signal duration of 10 s, with heat stimulation improving classification rates to 98.96 % for the same interval. Finally, the matched filter-aided classification approach improved upon the heat stimulated results even further to obtain a 100 % accuracy on classifying the test set for a signal duration of 5 s. These findings 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.
|Journal||Postharvest Biology and Technology|
|State||Published - Nov 2021|
Bibliographical noteFunding Information:
This work was funded by the National Institute of Food and Agriculture (NIFA) , U.S. Department of Agriculture (USDA) under project award number: 2019-67021-29692; and the Kentucky Agricultural Experiment Station (KAES) .
© 2021 Elsevier B.V.
- Codling moth
- Machine learning
- Matched filter
ASJC Scopus subject areas
- Food Science
- Agronomy and Crop Science