scholarly journals Experimental Recognition System for Dirty Eggshell by Using Image Analysis Technique

2020 ◽  
Vol 8 (5) ◽  
pp. 1122-1126
Author(s):  
Abdullah Beyaz ◽  
Serdar Özlü ◽  
Dilara Gerdan
Keyword(s):  
Image Analysis ◽  
Experimental System ◽  
Recognition System ◽  
Agricultural Product ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
Chicken Eggshell ◽  
Labview Platform ◽  
Chicken Eggs

The present study was focused on the design and implementation of an experimental recognition system for dirty chicken eggshell by using an image analysis technique. Image analysis based observation and evaluation techniques can be used efficiently and effectively for agricultural product quality control. Dirt stains on eggs are the result of mainly by feces (black to light brown stains), uric acid (white stains), yolk, and blood. The experimental system was used to obtain dark level images of dirty stains of chicken eggs owing to feces. For this aim, the dirty chicken eggs which have dirty parts were put under a webcam, and dirtiness degree was evaluated by using developed image analysis software at the LabVIEW platform. For the experiment, 100 clean and 100 dirty eggs were used to accurate the determination of dark stains. The results of the research showed that the designed experimental system pointed an accuracy of 99.8% at painted grade eggs. On the other hand, the accuracy of the differentiation of the dirt stains by feces was 98.5%. The developed system can be upgraded for developing egg sorting machines by presence-absence of dirty stains in eggshell.

A Novel User-Friendly Image Analysis Technique for Determination of Electrospun Nanofibrous Mats Thickness

NANO ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. 1750130 ◽  
Author(s):  
Bentolhoda Hadavi Moghadam ◽  
Shohreh Kasaei ◽  
A. K. Haghi
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Depth Estimation ◽  
Thickness Measurement ◽  
Relative Depth ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
The Cross ◽  
User Friendly

In this study, a novel technique for measuring the thickness of electrospun nanofibrous mat based on image analysis techniques is proposed. The thicknesses of electrospun polyacrylonitrile (PAN), polyvinyl alcohol (PVA), and polyurethane (PU) nanofibrous mats are calculated using depth estimation in different views. The images are captured by a fixed scanning electron microscope (SEM) where the mat sample is rotated by 15[Formula: see text], 30[Formula: see text], and 45[Formula: see text] angles. By calculating the disparity value (the distance between two corresponding points in two images), the relative depth of images and consequently the thickness of nanofibrous mat are obtained. Furthermore, the thickness of three electrospun mats are measured from the cross-section view of the nanofibrous mat by scanning the electron microscopy. A close agreement between results obtained by this method at low angle views (15[Formula: see text]) and the direct thickness measurement obtained from the cross-section view is achieved. Comparison of the average thickness from the direct measurement and the proposed method for different samples exhibits a linear relationship with the high regression coefficient of 0.96. By using the proposed method, the quantitative evaluation of the thickness measurement becomes feasible over the entire surface of electrospun mats.

scholarly journals 205 Non-destructive Surface Area Determination of Vegetables

HortScience ◽  
2000 ◽  
Vol 35 (3) ◽  
pp. 426C-426
Author(s):  
M.K. Upadhyaya ◽  
N.H. Furness
Keyword(s):  
Image Analysis ◽  
Surface Area ◽  
Area Measurement ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
Surface Area Measurement ◽  
Non Destructive ◽  
Surface Area Determination ◽  
Area Determination

Surface area of cucumbers, carrots, parsnips, and beets was determined using the following non-destructive methods: Baugerod's method, Baugerod's method with inclusion of a factor correcting for substitution of weight for volume in the formula, and a novel image analysis method. Accuracy of the methods was ascertained by comparison with a direct shrink-wrap replica method of surface area measurement. Vegetables ranged in shape from cylindrical (cucumber and carrot) to conical (parsnip and beet). No difference in accuracy among methods of surface area determination was detected for carrots or beets. Baugerod's method and the image analysis technique differed significantly from the direct shrink-wrap replica technique for surface area determination of parsnips and cucumbers, respectively. Inclusion of a correction factor in Baugerod's method did not increase the accuracy of this method for any of the vegetables. The precision and repeatability of each method was determined by repeated measures analysis. Baugerod's method lost precision and repeatability for the conically shaped vegetables. Conversely, the shrink-wrap replica method lost precision and repeatability for the cylindrically shaped vegetables. The image analysis technique was precise and highly repeatable over the range of vegetable shapes. The development of a rapid, accurate, and precise non-destructive method of surface area measurement using image analysis techniques will provide a useful tool in the physiological study of vegetable products. Applicability of such a method over a range of vegetable shapes will be of additional value.

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