A machine vision system for egg weight detection was developed. Egg image was grabbed by a CCD camera and a frame grabber. An indicator composed of R, G, B intensity was used for image segmentation. A series of algorithms were developed to evaluate egg's vertical diameter, maximal horizontal diameter, upper horizontal diameter and nether horizontal diameter. Based on extracted four size features of vertical and maximal/upper/nether horizontal diameter, a regression model between egg's weight and its size was established using SAS, which was used to detect egg's weight. The experiment results indicated that, for egg weight detection on the machine vision system, the correlative coefficient of the regression model was 0.9781 and the absolute error was no more than ±3 g, which would be lower work load on human graders and an increased flexibility in the egg quality control process in egg's industrialization.
A machine vision system for real-time fruit quality inspection was developed. The system consists of a chamber,
a laser projector, a TMS-7DSP CCD camera (PULNIX Inc.), and a computer. A Meteor-II/MC frame grabber
(Matrox Graphics Inc.) was inserted into the slot of the computer to grab fruit images. The laser projector and the
camera were mounted at the ceiling of the chamber. An apple was put in the chamber, the spot of the laser
projector was projected on the surface of the fruit, and an image was grabbed. 2 breed of apples was test, Each
apple was imaged twice, one was imaged for the normal surface, and the other for the defect. The red component
of the images was used to get the feature of the defect and the sound surface of the fruits. The average value,
STD value and comentropy Value of red component of the laser scatter image were analyzed. The Standard
Deviation value of red component of normal is more suitable to separate the defect surface from sound surface
for the ShuijinFuji apples, but for bintang apples, there is more work need to do to separate the different surface
with laser scatter image.