Konovalov, D.A., Hillcoat, S., Williams, G., Birtles, R.A., Gardiner, N. and Curnock, M.I. (2018) Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks. Proceedings of the International Conference on Ocean Science and Technology (COST 2018), Chengdu, 1-3 June 2018, In Press. https://doi.org/10.13140/RG.2.2.12923.62245

has been cited by the following article:

• TITLE: Estimating Mass of Harvested Asian Seabass Lates calcarifer from Images

  AUTHORS: Dmitry A. Konovalov, Alzayat Saleh, Jose A. Domingos, Ronald D. White, Dean R. Jerry

  KEYWORDS: Aquaculture, Asian Seabass, Barramundi, Lates calcarifer, Computer Vision, Image Processing, Weight Estimation

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.6 No.3B, August 9, 2018

  ABSTRACT: Total of 1072 Asian seabass or barramundi (Lates calcarifer) were harvested at two different locations in Queensland, Australia. Each fish was digitally photographed and weighed. A subsample of 200 images (100 from each location) were manually segmented to extract the fish-body area (S in cm2), excluding all fins. After scaling the segmented images to 1mm per pixel, the fish mass values (M in grams) were fitted by a single-factor model (M=aS1.5, a=0.1695 )achieving the coefficient of determination (R2) and the Mean Absolute Relative Error (MARE) of R2=0.9819 and MARE=5.1%, respectively. A segmentation Convolutional Neural Network (CNN) was trained on the 200 hand-segmented images, and then applied to the rest of the available images. The CNN predicted fish-body areas were used to fit the mass-area estimation models: the single-factor model, M=aS1.5, a=0.170, R2=0.9819, MARE=5.1%; and the two-factor model, M= aSb, a=0.124, b=0.155, R2=0.9834, MARE=4.5%.