Identification of Honeybees with Paint Codes Using Convolutional Neural Networks

Gabriel Santiago-Plaza, Luke Meyers, Andrea P. Gómez-Jaime, Rafael Meléndez-Ríos, Fanfan Noel, José Agosto, Tugrul Giray, Josué A. Rodríguez-Cordero, Rémi Mégret

Abstract: This paper proposes and evaluates methods for the automatic re-identification of honeybees marked with paint codes. It leverages deep learning models to recognize specific individuals from images, which is a key component for the automation of wild-life video monitoring. Paint code marking is traditionally used for individual re-identification in the field as it is less intrusive compared to alternative tagging approaches and is human-readable. To assess the performance of re-id using paint codes, we built a mostly balanced dataset of 8062 images of honeybees marked with one or two paint dots from 8 different colors, generating 64 distinct codes, repeated twice on distinct individual bees. This dataset was used to perform an extensive comparison of convolutional network re-identification approaches. The first approach uses supervised learning to estimate the paint code directly; the second approach uses contrastive learning to learn an identity feature vector that is then used to que