Abstract: Based on Local Binary Pattern (LBP) features of wing and scutum images and the improved Adaboost algorithm, we developed“Automated Fruit Fly Identification System-Bactrocera, AFIS-B” for automatic identification of Bactrocera Macquart (Diptera: Tephritidae). The system consists of seven modules, which includes image acquisition, image cropping, image preprocessing, feature extraction, classifier design, taxa identification and outcome display. The results showed that LBP features are effective to the automatic identification of fruit flies. The AFIS-B system has good accuracy and robustness by identifying 8 Bactrocera spp., and the average recognition rate is more than 80%. We also did preliminary experiments under different conditions, such as inhomogeneous illumination, distorted posture, specimen partly damaged and different sample sizes. The results showed that the system has good robustness for the first three conditions, and the recognition rate usually positively relate to numbers of training sets for each species and negatively relate to the total species numbers. This research provides the theoratical, method and data foundation for the construction and practice of automated identification system of fruit fly, and it also gives a reference to the research and construction of other insects automated identification systems. 

Key words: Tephritidae, Bactrocera, digital image, LBP feature, Adaboost algorithm, automatic identification system