Classification of coastal shrimp species using deep learning InceptionResNetV2 with data augmentation

Abstract

Coastal areas that are rich in shrimp natural resources usually have a strong fishing industry. Shrimp consumption can contribute to the local economy. Several types of shrimp have higher utilization potential in the fishing industry and human consumption. Classification helps sort out shrimp species in coastal waters. Deep learning technology helps identify and separate shrimp species through deep learning with the Convolutional neural network. The seven classes observed were Vaname Shrimp, Tiger Shrimp, Jerbung Shrimp, Giant Prawns, Red Shrimp, Ronggeng Shrimp, and Prawns. The data used is 50 per class, so 350 images are used. Due to dataset limitations, the proposed contribution is an initial Resnet architecture with augmentation. Resnet helps perform in-depth training to save training computation time, and improvements add variety to limited amounts of data. The result is that the average accuracy of the model is 99.4%, with an average training computation time of 7 and a half minutes. The MSE misclassification value was 0.0143, RMSE was 0.1195, and MAE was 0.0086. Testing tests and testing data on the model only takes 1-2 seconds. Prediction accuracy ranges from 90.01%-99.8%.