Dan Jeric Arcega Rustia

Lorenzo S. Querol, Macario O. Cordel, Dan Jeric A. Rustia et al.

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.

Bart M. van Marrewijk, Charbel Dandjinou, Dan Jeric Arcega Rustia et al.

Optimizing deep learning models requires large amounts of annotated images, a process that is both time-intensive and costly. Especially for semantic segmentation models in which every pixel must be annotated. A potential strategy to mitigate annotation effort is active learning. Active learning facilitates the identification and selection of the most informative images from a large unlabelled pool. The underlying premise is that these selected images can improve the model's performance faster than random selection to reduce annotation effort. While active learning has demonstrated promising results on benchmark datasets like Cityscapes, its performance in the agricultural domain remains largely unexplored. This study addresses this research gap by conducting a comparative study of three active learning-based acquisition functions: Bayesian Active Learning by Disagreement (BALD), stochastic-based BALD (PowerBALD), and Random. The acquisition functions were tested on two agricultural datasets: Sugarbeet and Corn-Weed, both containing three semantic classes: background, crop and weed. Our results indicated that active learning, especially PowerBALD, yields a higher performance than Random sampling on both datasets. But due to the relatively large standard deviations, the differences observed were minimal; this was partly caused by high image redundancy and imbalanced classes. Specifically, more than 89\% of the pixels belonged to the background class on both datasets. The absence of significant results on both datasets indicates that further research is required for applying active learning on agricultural datasets, especially if they contain a high-class imbalance and redundant images. Recommendations and insights are provided in this paper to potentially resolve such issues.