Implementation of coupled pattern recognition and regression artificial neural networks for mass estimation of headless‐shell‐on shrimp with random postures

Abstract

AbstractIn an actual sorting process, shrimps are fed to a machine‐vision‐based sorter at random postures. This study proposed an Enhanced Artificial Neural Network (E‐ANN) coupled with a Pattern Recognition ANN (P‐ANN) model to overcome the posture‐specificity of the regression ANN model commonly used for mass estimation of the headless‐shell‐on (HSO) shrimps. Images of 103 shrimps with seven different postures were used. The similarity of any shrimp image to the reference shrimp postures (i.e., extended‐legs, collapsed‐legs, curl body, and dorsal body) was determined by the P‐ANN model and used as additional input, besides the area and perimeter. The coupled‐ANN model could accurately estimate the mass of shrimps with random postures (R2 = 0.70 to 0.88 and MRE = −2.62 to 2.97%) within ~10 ms per shrimp, which is practical to use in an automatic shrimp sorting system based on machine vision technique. Further enhancement of the model performance could be achieved by adding color and texture features to distinguish different shrimp parts (e.g., body, legs, and tail).Practical ApplicationsThis research shows the potential of using a pattern recognition artificial neural network (P‐ANN) to cut the limitation of the conventional regression ANN model based on area and perimeter for shrimp mass estimation, which is specific to shrimp posture (or shape). Since the coupled P‐ANN and enhanced artificial neural network (E‐ANN) used a relatively short processing time (~10 ms per shrimp) and yielded less than 1 g error in the mass estimation of the headless‐shell‐on (HSO) shrimps with random postures, it is possible to incorporate this coupled ANN model in an automatic shrimp sorting system based on machine vision technique.