Abstract
Shrimp is one of the most widely consumed aquatic species globally, valued for both its nutritional content and economic importance. Shrimp farming represents a significant source of income in many regions; however, like other forms of aquaculture, it is severely impacted by disease outbreaks. These diseases pose a major challenge to sustainable shrimp production. To address this issue, automated disease classification methods can offer timely and accurate detection. This research proposes a deep learning-based approach for the automated classification of shrimp diseases. A dataset comprising 1,149 images across four disease classes was utilized. Six pretrained deep learning models, ResNet50, EfficientNet, DenseNet201, MobileNet, ConvNeXt-Tiny, and Xception were deployed and evaluated for performance. The images background was removed, followed by standardized preprocessing through the Keras image pipeline. Fast Gradient Sign Method (FGSM) was used for enhancing the model robustness through adversarial training. While advanced augmentation strategies, including CutMix and MixUp, were implemented to mitigate overfitting and improve generalization. To support interpretability, and to visualize regions of model attention, post-hoc explanation methods such as Grad-CAM, Grad-CAM++, and XGrad-CAM were applied. Exploratory results demonstrated that ConvNeXt-Tiny achieved the highest performance, attaining a 96.88% accuracy on the test dataset. After 1000 iterations, the 99% confidence interval for the model is [0.953,0.971].
Abstract (translated)
虾是全球消费最广泛的水生物种之一，因其营养价值和经济价值而备受重视。虾养殖在许多地区代表着重要的收入来源；然而，与其他形式的水产养殖一样，它严重受到疾病暴发的影响。这些疾病对可持续虾类生产构成了重大挑战。为了解决这个问题，自动化的疾病分类方法可以提供及时且准确的检测。本研究提出了一种基于深度学习的方法来实现虾病的自动化分类。该数据集包含跨越四个疾病的类别共1,149张图像。部署并评估了六种预训练的深度学习模型：ResNet50、EfficientNet、DenseNet201、MobileNet、ConvNeXt-Tiny和Xception。在Keras图像处理流水线中进行了背景移除及标准化预处理。使用快速梯度符号法（FGSM）通过对抗性训练来增强模型的鲁棒性。此外，采用了包括CutMix和MixUp在内的高级数据增强策略以减少过拟合并提高泛化能力。为了支持可解释性和可视化模型关注区域，应用了诸如Grad-CAM、Grad-CAM++和XGrad-CAM等事后解释方法。探索性结果显示，ConvNeXt-Tiny取得了最佳性能，在测试数据集上达到了96.88%的准确率。经过1000次迭代后，该模型在99%置信区间内的表现范围为[0.953, 0.971]。
URL
https://arxiv.org/abs/2601.00832
