Calibrated Ensemble Deep Learning for Reliable Shrimp Disease Classification with Explainable AI

Abstract

Shrimp aquaculture is also faced with some great challenges due to the disease which can result in 100% mortality rate in shrimp if left untreated. This research discusses an in-depth assessment of advanced deep learning architectures of automated classification of diseases in shrimps based on EfficientNet_B3 models with the addition of calibrated ensemble methods and test-time augmentation (TTA). We tested eight convolutional neural network architectures that are state-of-the-art on a medium dataset of images of diseases of shrimp. EfficientNet_B1 and EfficientNet_B3 were the best models. Subsequently, we came up with advanced calibrated pipelines consisting of multi stage training, ensemble learning, temperature scaling and systematic TTA protocols. The accuracy of improved EfficientNet-B1 was 94% with a good brier score but the Expected Calibration Error and Maximum Calibration error was poor, while the test accuracy of calibrated EfficientNet_B3 ensemble was 90.5% with better calibration reliability with a good brier score, ECE and MCE of 0.0477, 0.1999 and 0.2186 respectively. We use a hybrid approach of architectural optimization, data augmentation, probabilistic calibration ,ensemble technique and explainable AI to provide robust and reliable disease classification to practical use in aquaculture. The results reveal that they have made significant improvements over baseline models and have set new records on automated detection systems of diseases in shrimps.