A Neural Network Based Automated Identification System For Biological Species

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Abstract0119578PlatnickThis award provides support for the development of a completely automated system capable of providing species-level identifications of organisms based on digital images of specimens. The technology uses an artificial neural network and can use, as input, images transmitted over the internet. At present, obtaining species-level identifications of specimens can be a difficult undertaking. The number of trained systematizes for most groups of organisms is low; the success rate of non-specialists trying to achieve accurate identifications is often lower. Such a system has the potential to radically increase the use of biodiversity information in conservation as well as science. This project is aimed at developing software technology capable of species identification using spiders as test subjects. The approach is based on digital images of taxonomically relevant structures of the organism taken through a camera-equipped dissecting microscope. The images are subjected to wavelet transformation, a procedure that extracts shape information from the image while removing less useful, high-resolution information. By this procedure, the image is reduced to a set of wavelet coefficients that can be supplied to a computing algorithm known as an artificial neural network. Such networks are capable of learning to classify objects. The goal is creation of a system with a 95% accuracy in identification.In the first phase of the project, three datasets will be used: images of two families of Australasian spiders (Trochanteriidae and Prodidomidae), and third set of images defined geographically rather than taxonomically. This geographic set includes specimens from three consecutive years of collecting at selected sites in Tennessee, and is typical of ecological inventory data. In the second phase, a web interface that allows submission of images for identification over the internet will be developed.At present, a severely limiting factor on our understanding of community structure, diversity, and how diversity relates to ecosystem function and resulting human services is the lack of experts capable of identifying biological specimens to species. Thus, the potential impact of automated identification is enormous. A system that can identify any species in a particular family, or from a particular area, without requiring the user to have more than the most basic knowledge of the organism to be identified, has the potential to drastically improve the efficiency and scope of biological inventories, and subsequent monitoring efforts.