Record Details

<p>Colour discernment of tomatoes using machine vision system with OpenCV Python and Raspberry Pi</p>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p>Colour discernment of tomatoes using machine vision system with OpenCV Python and Raspberry Pi</p>
 
Added Entry - Uncontrolled Name Joice, Astina ; Department of Agricultural and Biosystems Engineering, NDSU, Fargo 58102, North Dakota, USA
Rajkumar, P. ; Department of Processing and Food Engineering, AEC&RI, TNAU, Kumulur 621 712, Tamil Nadu, India
Deepa, J ; Department of Food Processing and Engineering, AEC&RI, TNAU, Coimbatore 641 003, Tamil Nadu, India
Arulmari, R ; Department of Food Processing and Engineering, AEC&RI, TNAU, Coimbatore 641 003, Tamil Nadu, India
 
Uncontrolled Index Term Electrical/electronic engineering
Image evaluation, Machine Vision, Programming code, Raspberry Pi, Sorting
 
Summary, etc. <div class="WordSection1"><p style="text-align: justify;">Colour is the first quality attribute of food that consumers examine and it is an important component of food quality that influences market acceptance. Classification of fruits by visual inspection is an arduous, time-consuming process and prone to human error. The machine vision system is a distributed control system that integrates several machine vision modules with a control module and a user interface unit. This research proposes a method for recognising and sorting tomato fruits into a preferred location continuously. Before designing colour sorter the physical properties of fruits were studied. The major and minor diameter of the tomatoes ranges from 45-60mm and 35-50mm, respectively. The mean geometric diameter, sphericity and surface area were 48.64mm,0.94 and 6477.14mm<sup>2</sup> respectively. The average length, width, thickness, bulk density and true density were 54.63mm, 48.44mm, 51.42mm, 0.6874g/cm3 , 0.9852g/cm3 , respectively. The colour sorter was researched, designed and created with Raspberry Pi, USB camera, servo motor and different digital as well as mechanical components. The model used for Raspberry Pi is Raspberry Pi 3 Model B+, USB camera with a video resolution of 640 x 480, 4.2-6V servo motor. Image evaluation is completed on each captured picture and Raspberry Pi will do the selection of which fruit can be sorted. Specific programming code in Python is written for this system. The developed colour sorter captures images and diverts fruits into the respective channel at the rate of 1800 fruits/h (i.e.one fruit per 2 seconds).</p></div>
 
Publication, Distribution, Etc. Indian Journal of Engineering and Materials Sciences (IJEMS)
2022-08-25 03:20:50
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/IJEMS/article/view/55059
 
Data Source Entry Indian Journal of Engineering and Materials Sciences (IJEMS); ##issue.vol## 29, ##issue.no## 4 (2022): IJEMS-AUGUST 2022
 
Language Note en
 
Nonspecific Relationship Entry http://op.niscair.res.in/index.php/IJEMS/article/download/55059/465578302
 
Terms Governing Use and Reproduction Note Except where otherwise noted, the Articles on this site are licensed under Creative Commons License: CC Attribution-Noncommercial-No Derivative Works 2.5 India © 2015. The Council of Scientific &amp; Industrial Research, New Delhi.