Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/206423
Title: | Grain Quality Analysis using Image Processing Approach |
Researcher: | Mihir Narandas Dudhrejia |
Guide(s): | Chetan B Bhatt |
Keywords: | quotFeature Extraction, Grain Analysis, Image Acquisition, Image Processing, Quality Analysis,feature extraction, foreign elements. quot |
University: | Gujarat Technological University |
Completed Date: | 16-12-2017 |
Abstract: | quotThe significance of size and color is in consumer acceptance of grain appearance. The price of the item depends on the color of the final product. Colors are the first parameters newlineconsidered for quality by consumers. Consumer acceptance of grain and food highly depend upon the appearance. Appearance affects the quality of the grain. Wide research is going on for color and size measurement of the grain. Image analysis has proven the effective solution for measuring color based quality parameters. Though tedious, but it is very important to do the qualitative analysis and color easurement of the individual seed. Grain quality strongly connected with the health of human being. So the grain quality related research directly helps society. The research is to provide a solution for the large scale grain quality measurement and provide new convenient, harmless and non-destructive base approach for the quality parameters measurement techniques. Techniques are evolved which newlineeliminate the need of inefficient manual inspection and an automatic system relying on the machine vision is developed which allowed evaluating grain appearance quality. Research is done for size measurement and color calibration methods for image-based digital grain analysis. Size measurement is done from an angular position of the grain seeds. Color measurement is done for the colored base classification of grain seeds. The different grain organisation works with different grains and different grain varieties. So it is very difficult to provide different grain analysis solutions for them all. To deal with this, the calibration mechanism is provided. Different approaches are used for generating base data. This base data is prepared with the help of machine learning. This base data is then provided as input for actual process measurement. Experiments are carried out with different grain types, but with this research only focused at rice grain analysis. To realize the approach, a software mplementation is done in Microsoft .Net technology. The newline |
Pagination: | 7 MB |
URI: | http://hdl.handle.net/10603/206423 |
Appears in Departments: | Computer/IT Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 35.3 kB | Adobe PDF | View/Open |
02_certificate.pdf | 19.47 kB | Adobe PDF | View/Open | |
03_abstract.pdf | 30.58 kB | Adobe PDF | View/Open | |
04_declaration.pdf | 20.16 kB | Adobe PDF | View/Open | |
05_acknowledgement.pdf | 27.27 kB | Adobe PDF | View/Open | |
06_content.pdf | 40.42 kB | Adobe PDF | View/Open | |
07_list of tables.pdf | 22.16 kB | Adobe PDF | View/Open | |
08_list of figures.pdf | 28.02 kB | Adobe PDF | View/Open | |
09_list of abbreviation.pdf | 18.98 kB | Adobe PDF | View/Open | |
10_chapter 1.pdf | 57.48 kB | Adobe PDF | View/Open | |
11_chapter 2.pdf | 522.64 kB | Adobe PDF | View/Open | |
12_chapter 3.pdf | 113.22 kB | Adobe PDF | View/Open | |
13_chapter 4.pdf | 39.57 kB | Adobe PDF | View/Open | |
14_chapter 5.pdf | 57.01 kB | Adobe PDF | View/Open | |
15_chapter 6.pdf | 67.64 kB | Adobe PDF | View/Open | |
16_chapter 7.pdf | 203.38 kB | Adobe PDF | View/Open | |
17_chapter 8.pdf | 2.63 MB | Adobe PDF | View/Open | |
18_chapter 9.pdf | 265.72 kB | Adobe PDF | View/Open | |
19_conclusion.pdf | 44.69 kB | Adobe PDF | View/Open | |
20_references.pdf | 78.54 kB | Adobe PDF | View/Open | |
final thesis.pdf | 6.79 MB | Adobe PDF | View/Open |
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