# Automatic Weight Estimation of Harvested Fish from Images

arXiv: https://arxiv.org/abs/1909.02710 · HTML: https://mlsanity.com/paper/1909.02710/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey](https://mlsanity.com/paper/2206.05394/) (2022)
- [How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach](https://mlsanity.com/paper/2208.10662/) (2022)
- [Prawn Morphometrics and Weight Estimation from Images using Deep Learning for Landmark Localization](https://mlsanity.com/paper/2307.07732/) (2023)

## Abstract (arXiv)

Approximately 2,500 weights and corresponding images of harvested Lates calcarifer (Asian seabass or barramundi) were collected at three different locations in Queensland, Australia. Two instances of the LinkNet-34 segmentation Convolutional Neural Network (CNN) were trained. The first one was trained on 200 manually segmented fish masks with excluded fins and tails. The second was trained on 100 whole-fish masks. The two CNNs were applied to the rest of the images and yielded automatically segmented masks. The one-factor and two-factor simple mathematical weight-from-area models were fitted on 1072 area-weight pairs from the first two locations, where area values were extracted from the automatically segmented masks. When applied to 1,400 test images (from the third location), the one-factor whole-fish mask model achieved the best mean absolute percentage error (MAPE), MAPE=4.36%. Direct weight-from-image regression CNNs were also trained, where the no-fins based CNN performed best on the test images with MAPE=4.28%.