Image Processing Reference
FIGURE 8 Processing a table-fish trout image for classification using our statistical measur-
For this research, we have designed and implemented a functional prototype to collect RGB
trout images. This prototype includes canalization, illumination, and vision components that
have been meticulously assembled. Also, we believe that this prototype could be easily integ-
rated into a mechanical system to interconnect lined earth tanks in farms.
Our experimental results encourage our research as they have shown that our classification
system is effective, where 95.93%, 93.21%, and 96.25% precisions are observed when classify-
ing fry, fingerling, and table-fish trout, respectively.
It is important to observe that, although our statistical approach has been inspired to meas-
ure rainbow trout, this approach can be applied to other fishes grown in farms.
As part of our future work, we are integrating water flow and stereovision into our proto-
type to investigate two main issues: the light reflection into the water and the presence of tur-
bulence. Our final aim is to implement an economical classification system for small farms.
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