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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Automated species identification | 2/2 | https://en.wikipedia.org/wiki/Automated_species_identification | reference | science, encyclopedia | 2026-05-05T14:01:01.569883+00:00 | kb-cron |
"... we are running out of systematic palaeontologists who have anything approaching synoptic knowledge of a major group of organisms ... Palaeontologists of the next century are unlikely to have the luxury of dealing at length with taxonomic problems ... Palaeontology will have to sustain its level of excitement without the aid of systematists, who have contributed so much to its success."This expertise deficiency cuts as deeply into those commercial industries that rely on accurate identifications (e.g., agriculture, biostratigraphy) as it does into a wide range of pure and applied research programmes (e.g., conservation, biological oceanography, climatology, ecology). It is also commonly, though informally, acknowledged that the technical, taxonomic literature of all organismal groups is littered with examples of inconsistent and incorrect identifications. This is due to a variety of factors, including taxonomists being insufficiently trained and skilled in making identifications (e.g., using different rules-of-thumb in recognizing the boundaries between similar groups), insufficiently detailed original group descriptions and/or illustrations, inadequate access to current monographs and well-curated collections and, of course, taxonomists having different opinions regarding group concepts. Peer review only weeds out the most obvious errors of commission or omission in this area, and then only when an author provides adequate representations (e.g., illustrations, recordings, and gene sequences) of the specimens in question. Systematics too has much to gain from the further development and use of automated identification systems. In order to attract both personnel and resources, systematics must transform itself into a "large, coordinated, international scientific enterprise". Many have identified use of the Internet— especially via the World Wide Web — as the medium through which this transformation can be made. While establishment of a virtual, GenBank-like system for accessing morphological data, audio clips, video files and so forth would be a significant step in the right direction, improved access to observational information and/or text-based descriptions alone will not address either the taxonomic impediment or low identification reproducibility issues successfully. Instead, the inevitable subjectivity associated with making critical decisions on the basis of qualitative criteria must be reduced or, at the very least, embedded within a more formally analytic context.
Properly designed, flexible, and robust, automated identification systems, organized around distributed computing architectures and referenced to authoritatively identified collections of training set data (e.g., images, and gene sequences) can, in principle, provide all systematists with access to the electronic data archives and the necessary analytic tools to handle routine identifications of common taxa. Properly designed systems can also recognize when their algorithms cannot make a reliable identification and refer that image to a specialist (whose address can be accessed from another database). Such systems can also include elements of artificial intelligence and so improve their performance the more they are used. Once morphological (or molecular) models of a species have been developed and demonstrated to be accurate, these models can be queried to determine which aspects of the observed patterns of variation and variation limits are being used to achieve the identification, thus opening the way for the discovery of new and (potentially) more reliable taxonomic characters.
iNaturalist is a global citizen science project and social network of naturalists that incorporates both human and automatic identification of plants, animals, and other living creatures via browser or mobile apps. Naturalis Biodiversity Center in the Netherlands develops AI species identification models and service infrastructures, including but not limited to: A multi-source model trained with expert-validated data and used by 7 European biodiversity portals for citizen scientist projects in different countries across Europe; A model for analyzing images from insect camera DIOPSIS; 10 AI models for butterflies, cone snails, bird eggs, rays and sharks egg capsules, beach fossils as well as masks from different cultures that are in the collections of 5+ Dutch museums; (Animal) sound recognition models. Pl@ntNet is a global citizen science project which provides an app and a website for plant identification through photographs, based on machine-learning Leaf Snap is an iOS app developed by the Smithsonian Institution that uses visual recognition software to identify North American tree species from photographs of leaves. Google Photos can automatically identify various species in photographs. Plant.id is a web application and API made by FlowerChecker company which uses a neural network trained on photos from FlowerChecker mobile app.
== See also == Multi-access key Digital Automated Identification System
== References cited ==
== External links == Here are some links to the home pages of species identification systems. The SPIDA and DAISY system are essentially generic and capable of classifying any image material presented. The ABIS and DrawWing system are restricted to insects with membranous wings as they operate by matching a specific set of characters based on wing venation.
The SPIDA system ABIS DAISY DrawWing LeafSnap Archived 2013-05-20 at the Wayback Machine Pl@ntNet Insect.id by Kindwise recognizes over 6,000 species including beetles, spiders, centipedes, butterflies, ants, bees and other insect-like animals Mushroom id by Kindwise recognizes over 3,200 species including mushrooms, lichens and slime molds Plant.id by Kindwise recognizes more than 33,000 taxa, including houseplants, garden plants, trees, weeds, fungi, and lichens; it also recognizes common plant diseases