Abstract:
Dried fish is one of the major and traditional dishes of Sri Lankans and is also one of
the major animal protein sources with low cholesterol for humans. Despite, the
benefits and the popularity, people are facing challenges in identifying certain
varieties of dried fish which look the same such as Rastrelliger kanagurta (Kumbala),
Goldstriped sardinella (Salaya) and Sardina pilchardus (Keeri). When the above
three types of dried fish are mixed, the ability to differentiate one from another
becomes challenging and benefits between the three varieties is a concern. Consumers
have been misled by some sellers who manipulate the challenge of identification.
Therefore, the aim of this research is to identify the correct type of dried fish even if
those fish are mixed together with other types. To address the above issue, a machine
learning based solution was developed along with image processing using more than
3500 images. The developed model uses four features of dried fish, the head, trunk,
tail, and the entire body. Using YOLOv8 model, first we attempted to identify the
number of objects in the user captured image and we store those identified objects
temporarily within the model. Subsequently a trained VGG16 model is used for the
classification of dried fish types based on the previously identified objects. The model
achieved more than 90% overall accuracy in identifying the correct dried fish type.
In conclusion, the developed model can be used to effectively identify the type of
dried fish even in situations where they are mixed with other dried fish types. The
model will be further developed as a mobile application in the future for the
betterment of dried fish consumers as well as sellers.
