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.
Document Type
Article
Publication Date
2018
Digital Object Identifier (DOI)
https://doi.org/10.13031/trans.12548
Funding Information
This work was supported by the USDA National Institute of Food and Agriculture (Multistate Project No. 1007893).
Related Content
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.
Repository Citation
Li, Mengxing; Ekramirad, Nader; Rady, Ahmed; and Adedeji, Akinbode A., "Application of Acoustic Emission and Machine Learning to Detect Codling Moth Infested Apples" (2018). Biosystems and Agricultural Engineering Faculty Publications. 224.
https://uknowledge.uky.edu/bae_facpub/224
Included in
Bioresource and Agricultural Engineering Commons, Computer Sciences Commons, Plant Sciences Commons
Notes/Citation Information
Published in Transactions of the ASABE, v. 61, issue 3, p. 1157-1164.
© 2018 American Society of Agricultural and Biological Engineers
The copyright holder has granted the permission for posting the article here.