Here is a summary of the shrimp disease detection project:

Shrimp is an economically important seafood that is susceptible to viral infections that can spread rapidly and cause high mortality rates. Early diagnosis of disease outbreaks is critical for shrimp farmers.

This project applies deep learning techniques like convolutional neural networks (CNNs) to identify signs of disease in shrimp based on images. Different CNN models are compared to find the architecture that provides the highest accuracy for shrimp disease prediction.

• Conduct a comparative study between different deep learning techniques like CNNs and EfficientNet to evaluate accuracy of detecting signs of disease in shrimp
• Use image augmentation to expand the dataset
• Apply convolutional layers, pooling layers, filters etc. to tune the model for optimal accuracy

Shrimp infections can negatively impact breeding quality shrimp and be a health hazard if consumed. This project focuses on identifying key factors that contribute to shrimp infections. A core goal is comparing different deep learning approaches like CNNs and EfficientNet for detecting shrimp disease.

• Perform exploratory data analysis on the shrimp disease image dataset
• Use image augmentation techniques to expand the dataset
• Build and train CNN models like Convolutional Neural Networks and EfficientNetB0
• Tune hyperparameters like layers, filters etc. to optimize model accuracy
• Evaluate and compare model performance to select the best architecture

• Image augmentation helped create a larger dataset for improved model training
• Convolutional neural networks were effective for detecting signs of disease in shrimp images
• The EfficientNetB0 architecture provided the highest accuracy for this problem

Deep learning techniques like CNNs are promising for automating the diagnosis of shrimp diseases. With further refinement, this approach can help shrimp farmers identify and contain outbreaks early.