# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
## 1 Introduction  
The Chinese mitten crab (Eriocheir sinensis), also known as the hairy crab or freshwater crab, is one of the traditional Chinese aquatic treasures. Chinese mitten crabs cultured in Yangcheng Lake and other lakes are favored by people because of their excellent quality. However, in recent years, some unscrupulous traders have used \"bathing crabs\" to pass off inferior products as well-known lake products, which not only harmed the rights and interests of consumers, but also constituted a serious infringement on the brand of aquatic products. Accurate and efficient identification of the geographic origin of Chinese mittencrab has become a growing concern for researchers, which is of great significance for the assessment and management of aquatic resources. In order to reliably determine the geographical source of the Chinese mitten crab, researchers have proposed many methods, mainly divided into two categories: traditional methods of identification and those based on deep learning. The traditional methods mainly include morphological index identification and biochemical indicator identification, and so on. The morphological index identification method [1] mainly starts from the influence of water quality and food composition of the geographic origin on the external morphology of Chinese mitten crabs such as body length, body color, tooth spacing, preeiopod length, and other internal organ morphology and other indexes, and then determines the influence of different production environments on the morphological and physiological indexes of Chinese mitten crabs. However, this method often suffers from the problems of low detection efficiency and inaccurate detection results, and also requires a high level of experience for the inspectors, which is not favorable for the extension of the method. In addition, the biochemical index identification method based on biological indexes, the proportion of edible parts and nutrient composition, the element fingerprint identification method for identifying trace elements [2], and the electronic nose (tongue) identification method for detecting nitrogen compounds and non-nitrogen compounds as well as volatile substances are all common methods for identifying the geographic origin of Chinese mitten crab. For example, the Sr isotopic composition of the shells of Chinese mitten crabs can serve as a good geographic indicator because the 87 Sr/86 Sr ratios of the crabs are similar to the ratios in the water in which they grow [3]. [PERSON] et al. used a laser stripping technique combined with a multi-receiver inductively coupled plasma mass spectrometer (ICP-MS) to achieve an accurate determination of the geographic origin of Chinese mitten crabs by analyzing the strontium isotope composition of crab shells [4]. By conducting a multi-mineral fingerprinting analysis of the Chinese mitten crab in Yangcheng Lake throughout its annual cultivation cycle, [PERSON] et al. [5; 6] found that it takes a long time for the fingerprint characteristics of mineral elements to stabilize. By using the Agilent 7500 ce ICP-MS for a comparative analysis of multi-mineral elements in authentic crabs and those cultured for one month under controlled \"bathing\" conditions, they discovered significant differences in the profiles of 11 elements between the authentic and the cultured crabs, both before and after the bathing process. This finding serves as a foundation for verifying the origin of Chinese mitten crabs from Yangcheng Lake. Traditional methods are time-consuming, subject to significant human subjectivity, highly dependent on specialized expertise, and unsuitable for large-scale, rapid traceability applications. With the development of machine learning technology and image processing technology, there have been more choices for aquatic product monitoring and classification, and there are also more and more methods for aquatic product identification [7; 8; 9; 10; 11]. Among them, convolutional neural networks have great advantages in image feature extraction and classification.