Objective Rodents can survive in any existing habitat, and are the largest order of mammals in terms of both variety and quantity of species. Unlike other mammals, their classification can be variable and complex, due to the large number of species. Furthermore, the inter-species characteristics and traits tend to be convergent. They pose a seriously increasing threat to agriculture. Hence it is crucial to find effective measures to manage and control rodent infestation, which requires confirmation of their taxonomic statuses precisely and quickly. However, an expert on one species or family may be unfamiliar with another. These issues have increased the demand for digitized software tools that can recognize and characterize rodent skulls from images. In this study we developed a system, named “Rodents Skull Automatic Identification System”, based on random forests. Methods The training module and recognition module of the system were designed based on the recognition technology of computer science, OpenCV, and random forests. The training module included image input, image preprocessing, feature extraction, pattern recognition, and identification result return. The recognition module included image input, image preprocessing, feature extraction, training classifier, and classifier data storage. The system identified rodents of 13 species from 4 families through automatic extraction and analysis of 32 mathematical morphological features on dorsal maxillary images, such as eccentricity and compactness. Results The system could identify 13 species among 4 families of rodents. The results showed that the average identification accuracy rate was above 80%, and the identification accuracy rate of each species was above 70%, indicating that the system was highly reliable in recognition of rodents. Conclusion Rodents can be automatically identified with the aid of computer technology. However, this system is only a preliminary study, and it requires further studies to improve the recognition rate.

Carrying the identification characteristics, the biological specimen of specific vectors is an important basis for a border port to correctly identify the vector in intercepted subjects. With the help of computer and network technology, biological samples of vectors can be digitalized, based on which a database with quick inquiry functions may be created to facilitate vector identification. This paper presents the importance of vector samples digitalization and summarizes the advances in digital biological specimens and related researches at home and abroad, indicating that collection of digital biological samples should include both text and image data of the specimen. The workflow suitable for vector sample digitalization is also discussed.

【Abstract】 Under the support of asp program and structured query language（SQL） Server 2000 database management system, the information system on vector specimen was established by ADO data accessing technology and SQL query technology based on the data collected from the health department and the quarantine department. The system was made up of basic information database, specimen photo database and user information database. The information management system not only could take charge of the database, but also offered services to the ordinary users. It had good service function, which included that it had a friendly interface and it could inquire accurately and fuzzily. The system provided detailed information of vector specimens for the health/quarantine departments and the ordinary users, realizing the share of vector specimen information, which would be applied widely in the future.