Fruit flies are of huge biological and economic importance for the farming of different countries in the World, especially for Brazil. Brazil is the third largest fruit producer in the world with 44 million tons in 2016. The direct and indirect losses caused by fruit flies can exceed USD 2 billion, putting these pests as one of the biggest problems of the world agriculture. In Brazil, it is estimated that the economic losses directly related to production, the cost of pest control and in the loss of export markets, are between USD 120 and 200 million/year. The species of the genus Anastrepha are among the fruit flies economically important in the America tropics and subtropics with approximately 300 known species, of which 120 are recorded in Brazil. However, few species are economically important in Brazil and are considered pests of quarantine significance by regulatory agencies. In this sense, the development of automatic and semi-automatic tools for fruit fly species identification of the genus Anastrepha can assist the few existing specialists to reduce the insect analysis time and the economic losses related to these agricultural pests. We propose to apply mid-level image representations based on local descriptors for fruit fly identification tasks of three species of the genus Anastrepha. In our experiments, several local image descriptors based on keypoints and machine learning techniques have been studied for the target task. Furthermore, the proposed approaches have achieved excellent effectiveness results when compared with a state-of-the-art technique.