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At present, rapid, nondestructive, and objective identification of unqualified salted sea cucumbers with excessive salt content is extremely difficult. Artificial identification is the most common method, which is based on observing sea cucumber deformation during recovery after applying-removing pressure contact. This study is aimed at simulating the artificial identification method and establishing an identification model to distinguish whether the salted sea cucumber exceeds the standard by means of machine vision and machine learning technology. The system for identification of salted sea cucumbers was established, which was used for delivering the standard and uniform pressure forces and collecting the deformation images of salted sea cucumbers during the recovery after pressure removal. Image texture features of contour variation were extracted based on histograms (HIS) and gray level cooccurrence matrix (GLCM), which were used to establish the identification model by combining general regression neural networks (GRNN) and support vector machine (SVM), respectively. Contour variation features of salted sea cucumbers were extracted using a specific algorithm to improve the accuracy and stability of the model. Then, the dimensionality reduction and fusion of the feature images were achieved. According to the results of the models, the SVM identification model integrated with GLCM (GLCM-SVM) was found to be optimal, with accuracy, sensitivity, and specificity of 100%, 100%, and 100%, respectively. In particular, the sensitivity reached 100%, demonstrating an excellent identification ability to excessively salted sea cucumbers of the optimized model. This study illustrated the potential for identification of salted sea cucumbers based on pressure contact by combining image texture of contour varying with machine learning.

A New Approach for Unqualified Salted Sea Cucumber Identification: Integration of Image Texture and Machine Learning under the Pressure Contact