An intelligent identification system combining image and DNA sequence methods for fruit flies with economic importance (Diptera: Tephritidae).

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Abstract

Identification using images and DNA sequences are two important methods for identifying fruit fly species. In addition, the identification of insect species complexes is highly problematic when attempting to utilize automatic identification method in an actual environment. We integrated the image and DNA sequence identification methods into a single system for the first time and explored an open interactive multi-image comparison function for solving the problem of species complexes. AFIS1.0 (Automated Fruit Fly Identification System 1.0) was updated to AFIS2.0 (Automated Fruit Fly Identification System 2.0) by employing different models, and developing the system under a novel framework.
AFIS2.0 was developed using 83 species belonging to 8 genera in Tephritidae, which includes most pests of this family. The system applies Mask R-CNN (Mask Region Convolutional Neural Network) and discriminative deep metric learning (AlexNet based) methods for image identification, integrates Blast+ for DNA sequence comparison and specific weighting for the fusion result, respectively. At the species level, the best classification success rate for the wing images (as the Top-1 species in the species list of outcomes) reached 90%, and the average classification success rate for the wing, thorax, and abdomen images (as the Top-5 species in the species list of outcomes) attained 94%.
The system is more accurate and convenient than version 1.0 and can be beneficial for users with or without specific expertise regarding Tephritidae. It also provides a more compact and fluent computer system for fruit fly identification, and can be easily applied in practice. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.