2026-09-26T07:36:26Zhttps://mro.massey.ac.nz/server/oai/requestoai:mro.massey.ac.nz:10179/11752025-11-11T14:19:28Zcom_10179_294col_10179_296
DSpace at Massey University
author
Liu, MingHui
2010-02-08T20:07:55Z
NO_RESTRICTION2010-02-08T20:07:55Z
2009
http://hdl.handle.net/10179/1175
Q112881360
https://www.wikidata.org/wiki/Q112881360
Each year, the world’s top orange producers output millions of oranges for human
consumption. This production is projected to grow by as much as 64 million in 2010
and so the demand for fast, low-cost and precise automated orange fruit grading
systems is only deemed to become more increasingly important.
There is however an underlying limit to most orange blemish detection algorithms.
Most existing statistical-based, structural-based, model-based and transform-based
orange blemish detection algorithms are plagued by the following problem: any pixels
in an image of an orange having about the same magnitudes for the red, green and blue
channels will almost always be classified as belonging to the same category (either a
blemish or not). This however presents a big problem as the RGB components of the
pixels corresponding to blemishes are very similar to pixels near the boundary of an
orange. In light of this problem, this research utilizes a priori knowledge of the local
intensity variations observed on rounded convex objects to classify the ambiguous
pixels correctly. The algorithm has the effect of peeling-off layers of the orange skin
according to gradations of the intensity. Therefore, any abrupt discontinuities detected
along successive layers would significantly help identifying skin blemishes more
accurately. A commercial-grade fruit inspection and distribution system was used to
collect 170 navel orange images. Of these images, 100 were manually classified as
good oranges by human inspection and the rest are blemished ones. We demonstrate
the efficacy of the algorithm using these images as the benchmarking test set. Our
results show that the system garnered 96% correctly classified good oranges and 97%
correctly classified blemished oranges. The proposed system is easily customizable as
it does not require any training. The fruit quality bands can be adjusted to meet the
requirements set by the market standards by specifying an agreeable percentage of
blemishes for each band.
en
The Author
Pixels
Colour images
RGB colour
Grading algorithms
Navel orange blemish identification for quality grading system : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Computer Science at Massey University, Albany, New Zealand
Thesis
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