The aquaculture industry, strongly reliant on shrimp exports, faces challenges due to viral infections like the White Spot Syndrome Virus (WSSV) that severely impact output yields. In this context, computer vision can play a significant role in identifying features not immediately evident to skilled or untrained eyes, potentially reducing the time required to report WSSV infections. In this study, the challenge of limited data for WSSV recognition was addressed. A mobile application dedicated to data collection and monitoring was developed to facilitate the creation of an image dataset to train a WSSV recognition model and improve country-wide disease surveillance. The study also includes a thorough analysis of WSSV recognition to address the challenge of imbalanced learning and on-device inference. The models explored, MobileNetV3-Small and EfficientNetV2-B0, gained an F1-Score of 0.72 and 0.99 respectively. The saliency heatmaps of both models were also observed to uncover the "black-box" nature of these models and to gain insight as to what features in the images are most important in making a prediction. These results highlight the effectiveness and limitations of using models designed for resource-constrained devices and balancing their performance in accurately recognizing WSSV, providing valuable information and direction in the use of computer vision in this domain.

翻译：水产养殖业高度依赖虾类出口，但白斑综合征病毒（WSSV）等病毒感染严重影响产量，使其面临严峻挑战。在此背景下，计算机视觉可在识别专业人员或未受训人员不易察觉的特征方面发挥重要作用，从而可能缩短WSSV感染报告所需时间。本研究针对WSSV识别中数据有限的问题，开发了一款专用数据采集与监测移动应用，用于构建图像数据集以训练WSSV识别模型，并提升全国范围内的疾病监测能力。研究还深入分析了WSSV识别问题，以应对不均衡学习与设备端推理的挑战。所探索的MobileNetV3-Small与EfficientNetV2-B0模型分别取得了0.72和0.99的F1分数。通过观察两个模型的显著性热力图，揭示了这些模型的“黑箱”特性，并明确了图像中对预测最重要的特征。这些结果凸显了在资源受限设备上使用模型的有效性与局限性，以及平衡其准确识别WSSV性能的关键，为本领域计算机视觉的应用提供了有价值的信息与方向。