The accurate identification of biological control agents is necessary for monitoring and preventing contamination in integrated pest management (IPM); however, this is difficult for non-taxonomists to achieve in the field. Many machine learning techniques have been developed for multiple applications (e.g., identification of biological organisms). Some phytoseiids are biological control agents for small pests, such as Neoseiulus barkeri Hughes. To identify a precise biological control agent, a boosting machine learning classification, namely eXtreme Gradient Boosting (XGBoost), was introduced in this study for the semi-automated identification of phytoseiid mites. XGBoost analyses were based on 22 quantitative morphological features among 512 specimens of N. barkeri and related phytoseiid species. These features were extracted manually from photomicrograph of mites and included dorsal and ventrianal shield lengths, setal lengths, and length and width of spermatheca. The results revealed 100% accuracy rating, and seta j4 achieved significant discrimination among specimens. The present study provides a path through which skills and experiences can be transferred between experts and non-experts. This can serve as a foundation for future studies on the automated identification of biological control agents for IPM.