Classification of Boiled Shrimp's Shape Using Image Analysis and Artificial Neural Network Model

The image analysis technique and artificial neural networks (ANNs) for boiled shrimp's shape classification were developed in this research. A color image of boiled shrimp in red-green-blue format was processed and analyzed to determine the shape feature as a relative internal distance (RID). The RID was the ratio between the shortest distance measured perpendicularly between the center line and the shrimp's contour. The RID values from different 62 locations were calculated. The multilayer ANN models were trained to classify shapes of the boiled shrimp using the RID values as the network input. The analysis showed that the 15-node ANN model was highly effective for boiled shrimp's shape classification with 99.80% overall accuracy (regular-shaped shrimps [100.00%], shrimps with no tails [100.00%], with one tail [97.78%] and with broken body [100.00%]). The RID values were considered as an appropriate shape representation for boiled shrimps. The ANN model recognized most boiled shrimp's shape through the RID profiles. Practical Application The developed image analysis technique and artificial neural network model effectively classified the boiled shrimp's shape using the relative internal distance values. This technique can be used in the development of an automatic sorting system for boiled shrimp based on real-time image processing.