Assessing the potential for deep learning and computer vision to identify bumble bee species from images.

ABSTRACT: Pollinators are undergoing a global decline. Although vital to pollinator conservation and ecological research, species-level identification is expensive, time consuming, and requires specialized taxonomic training. However, deep learning and computer vision are providing ways to open this methodological bottleneck through automated identification from images. 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. 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. Among these models, InceptionV3 presented a good balance of accuracy (91.6%) a

SUBMITTER: Spiesman BJ 

PROVIDER: S-EPMC8027374 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature