A deep learning model to recognize food contaminating beetle species based on elytra fragments

Insect pests are often associated with food contamination and public health risks. Accurate and timely species-specific identification of pests is a key step to scale impacts, trace back the contamination process and promptly set intervention measures, which usually have serious economic impact. The current procedure involves visual inspection by human analysts of pest fragments recovered from food samples, a time-consuming and error-prone process. Deep Learning models have been widely applied for image recognition, outperforming other machine learning algorithms; however only few studies have applied deep learning for food contamination detection. In this paper, we describe our solution for automatic identification of 15 storage product beetle species frequently detected in food inspection. Our approach is based on a convolutional neural network trained on a dataset of 6900 microscopic images of elytra fragments, obtaining an overall accuracy of 83.8% in cross validation. Notably, the classification performance is obtained without the need of designing and selecting domain specific image features, thus demonstrating the promising prospects of Deep Learning models in detecting food contamination.