Automatic Weight Estimation of Harvested Fish from Images

TL;DR

This study develops and evaluates CNN-based methods for automatically estimating the weight of harvested fish from images, achieving high accuracy across different locations and segmentation strategies.

Contribution

The paper introduces CNN models trained on segmented fish masks and direct regression for weight estimation, demonstrating improved accuracy over traditional models.

Findings

Best model achieved MAPE of 4.28% on test images.

Segmentation-based and direct regression CNNs both effective.

Models generalize well across different locations.

Abstract

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%.