Automated rice classifier
The Automated Rice Classifier separates a 100g sample into three sub-samples, Headrice, Brokens, and Brewers by use of perforated sheets. It incorporates a vision system that captures the total surface area of each sub-sample and calculates the percentages of each sub-sample of the total sample. The...
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oai:animorepository.dlsu.edu.ph:etd_bachelors-152522021-11-11T05:57:26Z Automated rice classifier Chua, Mark Irwin K. Estonina, Christopher Gonzales, Francis M. Pangan, Jon Carlo G. Quia, Norman Nazer G. The Automated Rice Classifier separates a 100g sample into three sub-samples, Headrice, Brokens, and Brewers by use of perforated sheets. It incorporates a vision system that captures the total surface area of each sub-sample and calculates the percentages of each sub-sample of the total sample. The software then processes these percentages using NFA Standards for Milled Rice and determines into which grade the rice sample falls into. It consists of the shaker mechanism, a control box, and capturing pan area. The shaker mechanisms movements make use of pneumatic actuators. Movements that include the oscillating motion, tilting motion, and tray door opening motion. These pneumatics are controlled by the software via interfacing circuit using opto-isolators, a relay driver, and relays. The software implements these movements either through pre-programmed intervals or manual operation. The software filters the captured image contained in the capturing pan area. Using a histogram to more accurately determine threshold level the rice grain is separated from the background using a threshold filter. The total surface area is then counted. 2002-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/14610 Bachelor's Theses English Animo Repository Rice Rice--Processing--Machinery |
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Rice Rice--Processing--Machinery Chua, Mark Irwin K. Estonina, Christopher Gonzales, Francis M. Pangan, Jon Carlo G. Quia, Norman Nazer G. Automated rice classifier |
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The Automated Rice Classifier separates a 100g sample into three sub-samples, Headrice, Brokens, and Brewers by use of perforated sheets. It incorporates a vision system that captures the total surface area of each sub-sample and calculates the percentages of each sub-sample of the total sample. The software then processes these percentages using NFA Standards for Milled Rice and determines into which grade the rice sample falls into.
It consists of the shaker mechanism, a control box, and capturing pan area. The shaker mechanisms movements make use of pneumatic actuators. Movements that include the oscillating motion, tilting motion, and tray door opening motion. These pneumatics are controlled by the software via interfacing circuit using opto-isolators, a relay driver, and relays. The software implements these movements either through pre-programmed intervals or manual operation.
The software filters the captured image contained in the capturing pan area. Using a histogram to more accurately determine threshold level the rice grain is separated from the background using a threshold filter. The total surface area is then counted. |
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text |
author |
Chua, Mark Irwin K. Estonina, Christopher Gonzales, Francis M. Pangan, Jon Carlo G. Quia, Norman Nazer G. |
author_facet |
Chua, Mark Irwin K. Estonina, Christopher Gonzales, Francis M. Pangan, Jon Carlo G. Quia, Norman Nazer G. |
author_sort |
Chua, Mark Irwin K. |
title |
Automated rice classifier |
title_short |
Automated rice classifier |
title_full |
Automated rice classifier |
title_fullStr |
Automated rice classifier |
title_full_unstemmed |
Automated rice classifier |
title_sort |
automated rice classifier |
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Animo Repository |
publishDate |
2002 |
url |
https://animorepository.dlsu.edu.ph/etd_bachelors/14610 |
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1718382627311845376 |