关键词: 随机森林, 鼠类, 数学形态学, 自动识别

Abstract: 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.

Key words: Random forests, Rodent, Mathematical morphology, Automatic recognition