Poisonous Shrimp Detection System for Litopenaeus Vannamei using k-Nearest Neighbor Method

Abstract

One of the important seafoods in the food consumption of humans is shrimp. Although shrimp contains proteins that are needed by the human body, sometimes it contains toxins. This is due to environmental factors or catching processes that may use toxins. Therefore, the community should take precautions when consuming shrimp. White shrimp (Litopenaeus vannamei) is one type of shrimp that is preferred because of its delicious taste. The purpose of this research is to develop a computerized system for poisonous white shrimp detection. The category of white shrimps consists of two kinds, i.e., fresh white shrimps that are caught in a natural way (class A), and poisonous white shrimps that are caught by using toxin (class B). The features used are RGB colors (red, green, and blue) and texture (energy, contrast, correlation, and homogeneity). A similarity-based classification is performed by the k-Nearest Neighbor (k-NN) algorithm. The experiment was conducted on a dataset consisting of 90 white shrimp images. The holdout validation method was used to evaluate the system. The original dataset was divided into two parts, whereby 60 images were used as training samples and 30 images were used as testing images. Based on the evaluation results, it can be concluded that the classification accuracy is 73.33%. The benefit of the developed system is to help the community in selecting good and safe white shrimps.