Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning

Halil Bisgin; Tanmay Bera; Leihong Wu; Hongjian Ding; Neslihan Bisgin; Zhichao Liu; Monica Pava-Ripoll; Amy Barnes; James F Campbell; Himansi Vyas; Cesare Furlanello; Weida Tong; Joshua Xu

doi:10.3389/frai.2022.952424

Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning

Front Artif Intell. 2022 Aug 12:5:952424. doi: 10.3389/frai.2022.952424. eCollection 2022.

Authors

Affiliations

¹ Department of Mathematics and Applied Sciences, University of Michigan-Flint, Flint, MI, United States.
² Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.
³ Food Chemistry Lab 1, Arkansas Regional Laboratory, Office of Regulatory Affairs, US Food and Drug Administration, Jefferson, AR, United States.
⁴ Office for Food Safety, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, United States.
⁵ Stored Product Insect and Engineering Research Unit, US Department of Agriculture, Manhattan, KS, United States.
⁶ HK3 Lab, Milan, Italy.

Abstract

Food samples are routinely screened for food-contaminating beetles (i.e., pantry beetles) due to their adverse impact on the economy, environment, public health and safety. If found, their remains are subsequently analyzed to identify the species responsible for the contamination; each species poses different levels of risk, requiring different regulatory and management steps. At present, this identification is done through manual microscopic examination since each species of beetle has a unique pattern on its elytra (hardened forewing). Our study sought to automate the pattern recognition process through machine learning. Such automation will enable more efficient identification of pantry beetle species and could potentially be scaled up and implemented across various analysis centers in a consistent manner. In our earlier studies, we demonstrated that automated species identification of pantry beetles is feasible through elytral pattern recognition. Due to poor image quality, however, we failed to achieve prediction accuracies of more than 80%. Subsequently, we modified the traditional imaging technique, allowing us to acquire high-quality elytral images. In this study, we explored whether high-quality elytral images can truly achieve near-perfect prediction accuracies for 27 different species of pantry beetles. To test this hypothesis, we developed a convolutional neural network (CNN) model and compared performance between two different image sets for various pantry beetles. Our study indicates improved image quality indeed leads to better prediction accuracy; however, it was not the only requirement for achieving good accuracy. Also required are many high-quality images, especially for species with a high number of variations in their elytral patterns. The current study provided a direction toward achieving our ultimate goal of automated species identification through elytral pattern recognition.

Keywords: convolutional neural networks; deep learning; food safety; food-contaminating beetle; image classification; machine learning; species identification.