  Fish products account for about 16 percent of the human diet worldwide, as of
2017. The counting action is a significant component in growing and producing
these products. Growers must count the fish accurately, to do so technological
solutions are needed. Two computer vision systems to automatically count
crustacean larvae grown in industrial ponds were developed. The first system
included an iPhone 11 camera with 3024X4032 resolution which acquired images
from an industrial pond in indoor conditions. Two experiments were performed
with this system, the first one included 200 images acquired in one day on
growth stages 9,10 with an iPhone 11 camera on specific illumination condition.
In the second experiment, a larvae industrial pond was photographed for 11 days
with two devices an iPhone 11 and a SONY DSCHX90V cameras. With the first
device (iPhone 11) two illumination conditions were tested. In each condition,
110 images were acquired. That system resulted in an accuracy of 88.4 percent
image detection. The second system included a DSLR Nikon D510 camera with a
2000X2000 resolution with which seven experiments were performed outside the
industrial pond. Images were acquired on day 1 of larvae growing stage
resulting in the acquisition of a total of 700 images. That system resulted in
an accuracy of 86 percent for a density of 50. An algorithm that automatically
counts the number of larvae was developed for both cases based on the YOLOv5
CNN model. In addition, in this study, a larvae growth function was developed.
Daily, several larvae were taken manually from the industrial pond and analyzed
under a microscope. Once the growth stage was determined, images of the larva
were acquired. Each larva's length was measured manually from the images. The
most suitable model was the Gompertz model with a goodness of fit index of R
squared of 0.983.
