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Detection Area of Disease on Hybrid Grouper Fish Body Using Image
Processing Techniques
Ku Norsyaziema Ku Harith
Supervisor: Dr. Ezmahamrul Afreen bin Awalludin
Bachelor of Applied Science (Fisheries)
School of Fisheries and Aquaculture Sciences
Fish disease is one of the important parts in fisheries study to determine status of fish
health. In the manual approach, to detect fish disease on its body is challenging task
and labor-intensive as well as time consuming. Therefore, an image processing
technique based on edge detection and blob processing is proposed to segment the
disease area on fish body automatically. This proposed method is an alternative
approach solution to the manual approach system specifically to determine disease
area on hybrid grouper. The total number of 20 samples dataset has been collected
from Pusat Pembenihan dan Asuhan Ikan Marin. Based on the experimental results,
there was a significant difference in the scores for health area (M=25.11, SD=8.59)
and unhealthy area (M=4.55, SD=2.14) conditions; t(2)=10.382, p=0.05. The
proposed method is capable to estimate the fish disease area with less labor intensive
and less time consuming.
589 | UMT UNDERGRADUATE RESEARCH DAY 2018