Stingless Bee Classification: A New Dataset and Baseline Results

Bees play an important role as pollinating agents, contributing to the reproduction of many plant species around the world. Brazil is the home for different species of stingless bees, with around 200 registered species out of the more than 500 species classified worldwide. Each species constructs the entrance to its colony in an unique but similar way among colonies of the same species. In this work, we proposed a new dataset created in collaboration with stingless beekeepers from Brazil for the exploration of stingless bee species classification. The dataset consists of 158 samples distributed unequally among the 13 species: Boca de Sapo, Bora, Bugia, Irai, Japura, Jatai, Lambe Olhos, Mandaguari, Mirim Droryana, Mirim Preguica, Moca Branca, Mandacaia, and Tubuna. The results presented in this work were obtained using deep learning models (i.e. CNN architectures) such as VGG and DenseNet, which are commonly used for image classification task in different application domains. Pre-trained models from ImageNet were used, along with transfer learning techniques, and due to the small size of the dataset, data augmentation techniques were applied, resulting in an expanded dataset of 1,106 samples. The experimental results demonstrated that the DenseNet model achieved the best results, reaching an accuracy of 95%. The dataset created will be also made available as a contribution of these work. As far as we know, the stingless bee species identification task based on the colony entrance is addressed for the first time in this work.