Enhanced recurrent capsule network with hyrbid optimization model for shrimp disease detection

Disease detection plays an important role in shrimp aquaculture to ensure the health and sustainability of farming operations. Specifically, detecting viral infections at early stages can prevent significant losses. Image processing applications have been developed to detect different types of diseases in shrimp. However, theaccuracy of detection models needs improvement to detect various diseases through a single model. Therefore, this research presents a novel disease detection model using an Enhanced Recurrent Capsule Network (ERCN) with a hybrid optimization model for enhanced detection performance. The proposed ERCN utilizes dynamic routing of capsules to extract spatial hierarchies and patterns in shrimp images, while the recurrent layer extracts temporal dependencies. Performance is further improved by incorporating spatial and channel attention models to select optimal regions and features in the images for the fusion process. The dual-level feature fusion procedure combines local and global features, providing a final fused data to classify different types of diseases. Additionally, the proposed work incorporates a hybrid optimization that combines Harris Hawks Optimization (HHO) with the Marine Predator Algorithm (MPA) to fine-tune the classifier model parameters. Experiments evaluate the performance of the proposed disease detection model through various metrics such as accuracy, precision, recall, specificity, Matthews correlation coefficient, and F1-score. The resutls confirms that the performance of the proposed model is superior with precision of 94.9%, recall of 93.5%, F1-score of 94.6% and detection accuracy of 95.2% over conventional Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Long Short Term Memory (LSTM) Networks.