Application of acoustic emission and machine learning to detect codling moth infested apples

M. Li, N. Ekramirad, A. Rady, A. Adedeji

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. ‘GoldRush’ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect the data emitted by 80 larvae-infested and intact apples in total. Eleven AE features that changed with signaling time were obtained with the AE system. For each feature, the area under the curve along the signaling time was calculated and used as an independent input variable for the machine learning algorithms, which included linear discriminant analysis (LDA) and ensemble method adaptive boosting. With signaling times ranging from 0.5 to 120 s, classification rates for infested versus intact apples ranged from 91% to 100% for the training set and from 83% to 100% for the test set. The quick signal collection and high classification accuracy obtained in this study show the potential of AE for detecting and classifying CM-infested apples.

Original languageEnglish
Pages (from-to)1157-1164
Number of pages8
JournalTransactions of the ASABE
Volume61
Issue number3
DOIs
StatePublished - 2018

Bibliographical note

Funding Information:
The information reported in this paper (#:17-05-035) is a project of the Kentucky Agricultural Experiment Station and it is published with the approval of the Director. This work was supported by the USDA National Institute of Food and Agriculture (Multistate Project No. 1007893). We also acknowledge Dr. John Stencel (Tribo Flow Separations LLC, Lexington, Ky.), Dr. Clair Hicks (Department of Animal and Food Science, University of Kentucky), and Dr. Fred Payne (Department of Biosystems and Agricultural Engineering, University of Kentucky) for their contributions to the development of the AE system used for this project.

Publisher Copyright:
© 2018 American Society of Agricultural and Biological Engineers.

Keywords

  • Acoustic emission
  • Apple
  • Codling moth
  • Machine learning
  • Pest infestation

ASJC Scopus subject areas

  • Forestry
  • Food Science
  • Biomedical Engineering
  • Agronomy and Crop Science
  • Soil Science

Fingerprint

Dive into the research topics of 'Application of acoustic emission and machine learning to detect codling moth infested apples'. Together they form a unique fingerprint.

Cite this