To this end, we present BeeMachine, a web application that allows anyone to use our classification model to identify bumble bees in their own images.īees (Hymenoptera: Anthophila) serve a critical role in most terrestrial ecosystems as pollinators of crops and natural plant communities e.g., 1, 2, 3. Continued development of this technology for automatic species identification and monitoring has the potential to be transformative for the fields of ecology and conservation. However, error rates also depended on the level of morphological variability among individuals within a species and similarity to other species.

Species-level error rates were generally smaller for species represented by more training images. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) and average speed (3.34 ms). We gathered over 89,000 images of bumble bees, representing 36 species in North America, to train the ResNet, Wide ResNet, InceptionV3, and MnasNet models. Focusing on bumble bees, we compare four convolutional neural network classification models to evaluate prediction speed, accuracy, and the potential of this technology for automated bee identification.

However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. Pollinators are undergoing a global decline.

Bumble bee slot machine