Consumer satisfaction and food safety are prime concerns for seafood retailers and wholesalers. Shrimp and its products are popular all over the world and play a significant part in maintaining a healthy diet by delivering a variety of nutrients and health benefits. Fresh shrimp and shrimp products, on the other hand, are very perishable and vulnerable to the rapid formation of disagreeable scents and tastes, as well as a rapid decay process. Shrimp freshness has previously been determined using a variety of techniques, but earlier techniques lacked adequate precision. To overcome this issue, in this manuscript, a Hybrid Convolutional Neural Network (Hyb-CNN) and Support Vector Machine (SVM) optimized with Marine Predators Algorithm (MPA) for shrimp freshness detection (SFD-Hyb-CNN-SVM-MPA) is proposed for classifying the freshness shrimp and non-freshness shrimp. The real time dataset is given to mean curvature Flow (MCF) filtering method and its pre-processed images are given to the Hybrid Convolutional Neural Network and Support Vector Machine classifier for classifying the freshness shrimp and non-freshness shrimp. Generally, The Hyb-CNN- does not demonstrate the implementation of any optimization techniques for identifying the ideal parameters and assuring correct classification. The proposed Marine Predators Algorithm (MPA) is considered for optimizing the hyper parameter of Hyb-CNN and SVM which is executed in MATLAB and certain performance measures are used to assess the effectiveness of the proposed approach such as precision, recall, f-measure, accuracy, computation time. The proposed method attains lower computational time39.89%, 43.78%, and 52.67%, higher accuracy 21.35%, 18.56%, and 13.56% compared with the existing methods, like shrimp freshness detection using Deep shrimp Net (SFD-D-SHNet), shrimp freshness detection using artificial neural network and k-neighbour network (SFD-ANN-KNN), shrimp freshness detection using convolutional neural network (SVM-FCCD) respectively
