Non-destructive detection of codling moth infestation in apples using acoustic impulse response signals

Alfadhl Y. Khaled, Nader Ekramirad, Chadwick A. Parrish, Paul S. Eberhart, Lauren E. Doyle, Kevin Donohue, Raul T. Villanueva, Akinbode A. Adedeji

Research output: Contribution to journalArticlepeer-review

Abstract

Codling moth (CM) (Cydia pomonella L.) is the most destructive pest for apples, causing large economic losses when not properly mitigated. Efficient detection methods can limit the spread of this pest in the apple supply chain. Non-destructive methods have several advantages over the current methods in that they can be applied to every apple (or a much larger sample) thereby reducing the possibility of missed detection. This paper examines the feasibility of acoustic impulse response methods for detecting CM larvae-infested apples. Experiments were performed on control and artificially infested apples from three different cultivars. Signals were recorded with a contact sensor, and 21 signal features were proposed and extracted to characterise relevant properties of the response. The 21 features were evaluated with 11 machine leaning algorithms to determine if the features or their subsets contained information that could reliability determine if an apple was/is infested. Classification test results using a 10-fold cross-validation indicated accuracy rates between 80% and 92% for Fuji apples, between 92% and 99% for Gala apples, and 63% and 97% for Granny Smith apples. The impulse response required between 60 and 80 ms for each apple (not counting setup/transition time). These results from this study suggest that active impulse response classification can potentially improve the detection of post-harvest apple CM infestation detection along the supply chain.

Original languageEnglish
Pages (from-to)68-79
Number of pages12
JournalBiosystems Engineering
Volume224
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Alfadhl Alkhaled reports financial support was provided by University of Kentucky. Alfadhl Alkhaled reports a relationship with University of Kentucky that includes: employment. Alfadhl Alkhaled has patent pending to None.

Funding Information:
This work was funded by the National Institute of Food and Agriculture (NIFA), United States Department of Agriculture (USDA) under project award number: 2019-67021-29692 . The authors also acknowledge other funding support from the Kentucky Agricultural Experiment Station (KAES) , and technical support from Signal Solutions, LLC Kentucky .

Publisher Copyright:
© 2022 IAgrE

Keywords

  • Acoustic impulse signals
  • Apple
  • Codling moth
  • Machine learning
  • Non-destructive testing
  • Pest infestation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Food Science
  • Agronomy and Crop Science
  • Soil Science

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