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

Application for White Spot Syndrome Virus (WSSV) Monitoring using Edge Machine Learning