An Improved Method to Obtain Fish Weight Using Machine Learning and NIR Camera with Haar Cascade Classifier

The calculation of weight and mass in aquaculture systems is of great importance, since with this task, it is decided when to harvest; generally, the above is manipulating the body manually, which causes stress in the fish body. Said stress can be maintained in the fish body for several hours. To solve this problem an improved method was implemented using artificial intelligence, near-infrared spectroscopy camera, Haar classifiers, and a mathematical model. Hardware and software were designed to get a photograph of the fish in its environment in real conditions. This work aimed to obtain fish weight and fish length in real conditions to avoid the manipulation of fish with hands for the process mentioned, avoiding fish stress, and reducing the time for these tasks. With the implemented hardware and software adding an infrared light and pass band filter for the camera successfully, the fish was detected automatically, and the fish weight and length were calculated moreover the future weight was estimated.

Automated summary

The calculation of weight and mass in aquaculture systems is significant for determining the timing of harvest. However, the manual manipulation of fish causes stress and can last for hours. To address this issue, an improved method utilizing artificial intelligence, near-infrared spectroscopy camera, Haar classifiers, and a mathematical model was implemented. With the designed hardware and software, fish weight and length can be automatically detected and calculated in real conditions, reducing the need for manual manipulation and minimizing fish stress.