Counting tilapia larvae using images captured by smartphones

Abstract

In this work, we propose a new way for automatically counting fish larvae in Petri dishes using images captured by a standard smartphone. A new tilapia larvae image dataset for training and validating machine learning models has been created and used to validate a recent machine learning approach based on multi-stage model refinement of confidence maps. A mean absolute error (MAE) of 1.43 has been achieved using the proposed automatic larvae counter, indicating that the proposed approach is promising for larvae counting, as the mean number of larvae per image is more than 20. The proposed approach also achieved precision, recall, and F-measure values of 0.98, 0.92, and 0.95, respectively, for larvae detection using a dataset containing images from more than 6,000 manually annotated larvae.

Keywords

• Tilapia aquaculture
• Precision livestock technology
• Computer vision
• Larvae counting