(3) scaling—studies consider only small numbers of species (<200 species), and (4) novel species — systems are restricted to the species they have been trained for and will classify any novel observation as one of the known species. A survey published in 2017 systematically compares and discusses progress and findings towards automated plant species identification within the last decade (2005–2015). 120 primary studies have been published in high-quality venues within this time, mainly by authors with computer science background. These studies propose a wealth of computer vision approaches, i.e., features reducing the high-dimensionality of the pixel-based image data while preserving the characteristic information as well as classification methods. The vast majority of these studies analyzes leaves for identification, while only 13 studies propose methods for flower-based identification. The reasons being that leaves can easier be collected and imaged and are available for most of the year. Proposed features capture generic object characteristic, i.e., shape, texture, and color as well as leaf-specific characteristics, i.e., venation and margin. The majority of studies still used datasets for evaluation that contained no more than 250 species. However, there is progress in this regard, one study uses a dataset with >2k and another with >20k species. A system developed in 2022 showed that automated identification achieves accuracy that is sufficiently high for being used in an automated insect surveillance system using electronic traps. By training classifiers on a few hundred images it correctly identified fruit-flies, and can be used for continuous monitoring aimed at detecting species invasion or pest outbreak. Several aspects contribute to the success of this system. Primarily, using e-traps provide a standardized setting, which means that even though they are deployed in different countries and regions, the visual variability, in terms of size view angle and illumination are controlled. This suggests that trap-based systems may be easier to develop than free-view systems for automatic pest identification. There is a shortage of specialists who can identify the very biodiversity whose preservation has become a global concern. In commenting on this problem in palaeontology in 1993, Roger Kaesler recognized: "... 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