We present a visually based method for the taxonomic identification of benthic invertebrates that automates image capture, image processing, and specimen classification. The BugID system automatically positions and images specimens with minimal user input. Images are then processed with interest operators (machine-learning algorithms for locating informative visual regions) to identify informative pattern features, and this information is used to train a classifier algorithm. Naïve Bayes modeling of stacked decision trees is used to determine whether a specimen is an unknown distractor (taxon not in the training data set) or one of the species in the training set. When tested on images from 9 larval stonefly taxa, BugID correctly identified 94.5% of images, even though small or damaged specimens were included in testing. When distractor taxa (10 common invertebrates not present in the training set) were included to make classification more challenging, overall accuracy decreased but generally was close to 90%. At the equal error rate (EER), 89.5% of stonefly images were correctly classified and the accuracy of nonrejected stoneflies increased to 96.4%, a result suggesting that many difficult-to-identify or poorly imaged stonefly specimens had been rejected prior to classification. BugID is the first system of its kind that allows users to select thresholds for rejection depending on the required use. Rejected images of distractor taxa or difficult specimens can be identified later by a taxonomic expert, and new taxa ultimately can be incorporated into the training set of known taxa. BugID has several advantages over other automated insect classification systems, including automated handling of specimens, the ability to isolate nontarget and novel species, and the ability to identify specimens across different stages of larval development.
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