Background: White Spot Syndrome Virus (WSSV) is a major pathogen in shrimp aquaculture, causing severe economic losses. The early detection of WSSV is essential for managing outbreaks. Traditional diagnostic methods are effective but often slow and resource-intensive. This study investigates the DenseNet201-Convolution Neural Network model for efficient WSSV detection. 
 
Methods: An online dataset of shrimp images was prepared, including healthy and WSSV-infected samples. Images were preprocessed and fed into DenseNet201 - convolutional neural network. The model was fine-tuned for WSSV detection. Its performance was evaluated using classification metrics. 
 
Result: The model demonstrated high performance, achieving a training accuracy of 99.8% and a validation accuracy of 97%. Precision, recall and F1 score for the WSS class were 97.06%, 94.29% and 95.65%, respectively, while for the healthy class, they were 93.10%, 96.43% and 94.74%. The overall accuracy reached 95.24%, with an MCC of 0.905. The ROC curve showed an AUC of 1 for both classes, indicating perfect classification performance. The DenseNet201-based CNN model successfully detects WSS in shrimp with high accuracy and generalizability. This approach provides a robust tool for early disease detection in aquaculture, though future work should focus on dataset expansion and real-world validation to enhance the model&rsquo;s robustness under diverse conditions.

Application of Densenet201- Convolution Neural Network for Detection of White Spot Syndrome Virus (WSSV) in Shrimp to Enhance Aquaculture Disease Management

Background: Agriculture is a major driver of economic expansion and accounts for 17% of India&rsquo;s GDP. However, the loss of agricultural land is a major problem that affects the economy and farmers&rsquo; livelihoods. Farmers use non-scientific techniques to detect pomegranate diseases, which takes time. A detection and classification system based on DL provides a quicker and more precise answer. 
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Methods: This paper presents a deep learning-based framework for the categorization and detection of pomegranate fruit diseases. For disease identification, five Deep CNN models are used: VGG16, MobileNetV2, ResNet50V2, DenseNet121 and the suggested PomeNetV2. 5,099 photos from five classes in a custom pomegranate fruit dataset are used. Pre-processing methods like data augmentation, resizing and rescaling are used to improve model performance and reduce overfitting. After that, the dataset is divided into 20% for testing and 80% for training. 
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Result: The confusion matrix, precision, recall, accuracy and F1-score were used to evaluate the performance of the suggested models. DenseNet201 did marginally better with 98% accuracy, according to the data, while VGG16, MobileNetV2 and ResNet50V2 all attained 97% accuracy. The PomeNetV2 model achieved the greatest accuracy of 99.02%, outperforming all other models.

Application of Densenet201- Convolution Neural Network for Detection of White Spot Syndrome Virus (WSSV) in Shrimp to Enhance Aquaculture Disease Management

Performance Evaluation of Deep Learning Models for Multiclass Disease Detection in Pomegranate Fruits