The fishmeal adulteration identification based on microscopic image and deep learning

Abstract

The fishmeal adulteration identification based on microscopic image and deep learning was studied in this paper, which included the qualitative identification and component recognition of adulterated fishmeal. Mobilenetv2 was chosen as the qualitative identification model to distinguish between fishmeal samples and adulterated samples. And the accuracy, precision, recall, and F1-measure of the test set were adopted as the evaluation indicators. The YOLOv3-Mobilenetv2 was selected as the component recognition model to distinguish components in adulterated samples, in which the feature pyramid structure and multi-scale feature fusion strategy were applied to solve the multi-scale problem in object detection. And the mean average precision (mAP) was adopted as the precision evaluation index and the frame per second (FPS) as the speed evaluation index of the component recognition model. At the same time, the effects of image enhancement preprocessing and resolution on the identification of fishmeal adulteration were also considered in this paper. It was shown that Mobilenetv2 was more suitable for qualitative identification when using the local constrained mask transformed microscopic images with a resolution of 224 × 224 pixels. The average accuracy, precision, recall, and F1-measure of the model were 93.02%, 93.11%, 93.25%, and 92.69% respectively. The improved YOLOv3-Mobilenetv2 was more suitable for component recognition with a resolution of 608 × 608 pixels. The mAP reached 78.49%, and the FPS was 45.97 f·s−1. The model of fishmeal adulteration identification adopted in this paper had high accuracy, which could quickly identify adulterated fishmeal and recognize components, and provide a new method and reference for improving the objectivity of fishmeal adulteration identification.