Image recognition using convolutional neural networks for classification of honey bee subspecies
Dario De Nart, Cecília Costa, Gennaro Di Prisco, Emanuele Carpana
Abstract
Abstract Four models based on convolutional neural networks were used to investigate whether image recognition techniques applied to honey bee wings could be used to discriminate among honey bee subspecies. A dataset consisting of 9887 wing images belonging to 7 subspecies and one hybrid was analysed with ResNet 50, MobileNet V2, Inception Net V3, and Inception ResNet V2. Accuracy values of classification of individual wings were over 0.92, and all models outperformed traditional morphometric evaluation. The Inception models achieved the highest accuracies and higher scores of precision and recall for most classes. When wing images were grouped by colony, almost all wings in the colony samples were labelled with the same class. We conclude that automatic image recognition and machine learning applied to honey bee wings can reliably discriminate among the European subspecies and could thus represent a useful tool for fast classification of honey bee subspecies for breeding and conservation aims.