Application for White Spot Syndrome Virus (WSSV) Monitoring using Edge
  Machine Learning
Lorenzo S. Querol Macario O. Cordel II Dan Jeric A. Rustia Mary Nia M. Santos
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.