Bird nest shape quality assessment using machine vision system
Date
2012-02-27Author
Fathinul Syahir, Ahmad Sa'ad
Ali Yeon, Md Shakaff, Prof. Dr.
Mohd Zulkifly, Abdullah, Dr.
Ammar, Zakaria
Metadata
Show full item recordAbstract
Swiftlets are birds contained within the four genera
Aerodramus, Hydrochous, Schoutedenapus and Collocalia. They
form the Collocaliini tribe within the swift family Apodidae.
Swiftlet nest economy is currently envisaged to contribute
significantly to foreign earnings of Malaysia. Many
establishments are currently engaged in bird nest farming and
trying to improve the quality and quantity of nest production.
The raw bird’s nest (unprocessed) can achieve up to RM 4,000
per kilos. Processed and cleaned bird’s nest can reach up to RM
9,000 or more per kilo. To date, the bird nest grading is based on
weight and shape. The inspection and grading for raw edible bird
nest were performed visually by expert panels. This conventional
method is relying more on human judgments. Unfortunately, it is
a tedious process and often inconsistence from one person to
another. Bird nest has an approximately two-dimensional nature,
and, therefore they are most suitable for real-time machine
processing. This experiment was performed on various camera
angel and bird nest position. More than hundreds birds nest was
used in this experiment obtained throughout west peninsular
Malaysia. A Fourier-based shape separation (FD) method was
developed from CCD image data to grade bird nest by its shape
and size. FD was able to differentiate different shape such as
round (oval) and 'v' shaped depending on the swiftlet species and
geographical origin. Shape analysis was established using
multivariate discriminant analysis. The Wilks' lambda analysis
was invoked to transform and compress the data set comprising
of large number of interconnected variables to a reduced set of
variates. It can be further used to differentiate bird nest from
different geographical origin. Overall, the vision system was able
to correctly classify 100% of the V and Oval shaped and 81.3%
for each grade in oval shape of the bird nest. The performances
were compared with the expert panels and the results show that
this technique achieved similar accuracy.