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  5. Development of a machine vision system for rice seed inspection system
 
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Development of a machine vision system for rice seed inspection system

Journal
Food Research
Date Issued
2020-01-01
Author(s)
Ruslan R.
Universiti Putra Malaysia
Khairunniza-Bejo S.
Universiti Putra Malaysia
Rukunudin I.H.
Universiti Malaysia Perlis
Jahari M.
Universiti Putra Malaysia
Mohd Firdaus Ibrahim
Universiti Malaysia Perlis
DOI
10.26656/fr.2017.4(S6).049
Handle (URI)
https://hdl.handle.net/20.500.14170/6634
Abstract
Rice seed production in Malaysia is greatly dependent on the purity of the cultivated paddy seed produced through the government certified paddy seed program. The seeds to be marketed by the seed processors must undergo quality control protocol where the seed lots are sampled from the seed farms and seed processing plants for purity analysis by the enforcing agency at the Seed Testing Laboratory of the Department of Agriculture (DoA). The current inspection conducted by the laboratory is based on a manual process, which is laborious and time-consuming. Therefore, a prototype (Patent ID: PI2018500018) of a machine vision-based rice seed inspection system (RiSe-IViS) was developed to explore the possibility of replacing the existing manual method in distinguishing the weedy rice and cultivated rice seeds under the Standard Jabatan Pertanian Malaysia (SJPM) standard protocol with a modern, effective and efficient technique using an image processing approach. The developed RiSe-IViS prototype consists of two parts i) hardware configuration and ii) software development. This paper discussed the criteria to be established, challenges and limitation encountered in developing the hardware prototype involving the image acquisition setup, lighting configuration and seed plate design. The importance of each criterion to ensure its reproducibility are also discussed. A software programme was developed to assist the user for image acquisition and analysis. The image processing steps undertaken in the programme are also discussed. The RiSe-IViS is expected to classify major rice seed varieties available in Malaysia against the weedy rice variants with superior accuracy.
Subjects
  • Machine vision

  • Weedy rice

  • Machine learning

  • Image acquisition

  • Image processing

  • Classification

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