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

TL;DR

This paper presents a mobile-based computer vision approach for detecting White Spot Syndrome Virus in shrimp, addressing data scarcity and model interpretability to enhance aquaculture disease monitoring.

Contribution

Developed a mobile application for data collection and training WSSV recognition models, analyzing model performance and interpretability for resource-constrained devices.

Findings

EfficientNetV2-B0 achieved an F1-Score of 0.99.

MobileNetV3-Small achieved an F1-Score of 0.72.

Saliency heatmaps provided insights into model decision features.

Abstract

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.