Mohd Firdaus Ibrahim
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Preferred name
Mohd Firdaus Ibrahim
Official Name
Mohd Firdaus , Ibrahim
Alternative Name
Ibrahim, M. F.
Ibrahim, Mohd Firdaus
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Scopus Author ID
56571962400
Researcher ID
AAE-5507-2020

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  • Publication
    In-Line sorting of Harumanis Mango based on external quality using visible imaging
    ( 2016)
    Mohd Firdaus Ibrahim
    ;
    Fathinul Syahir Ahmad Sa'ad
    ;
    Ammar Zakaria
    ;
    Ali Yeon Md Shakaff
    The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.
      4  11
  • Publication
    Development of a machine vision system for rice seed inspection system
    ( 2020-01-01)
    Ruslan R.
    ;
    Khairunniza-Bejo S.
    ;
    Rukunudin I.H.
    ;
    Jahari M.
    ;
    Mohd Firdaus Ibrahim
    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.
      1  36
  • Publication
    Multi sensor system for classifying Harumanis mango based on external and internal quality
    ( 2016)
    Mohd Firdaus Ibrahim
    This thesis presents a multi sensor system for classifying Harumanis mango based on its external and internal quality. Both external and internal quality of Harumanis mango affects the consumer buying preferences. Current method of classifying Harumanis mango is done manually and destructive for its internal quality determination. The proposed system consists of two parts. First part is the external quality classification using machine vision system which is based on its shape and mass. The second part is the internal quality classification using near infrared (NIR) spectroscopy, based on its total soluble solid (TSS) value. An image acquisition platform was built to capture the 3- Dimensional image of Harumanis mango in a single acquisition. A real-time measurement calibration technique was developed in this research. Combination of Fourier descriptor parameters and size-shape parameters was used to recognize the shape of Harumanis mango. An improved two-dimensional disk method was used to estimate the volume of Harumanis mango based on the captures image. Then a correlation between the actual volume and actual mass was derived and used to estimate the mass of Harumanis mango on inline system. The proposed method can correctly classify the Harumanis mango according to its shape and mass 94.2% of the time. NIR spectrometer was used to obtain the reflectance wavelength of the Harumanis mango. The juice from the mango was obtained and measured with a refractrometer to obtain the actual TSS value. Then, the acquired NIR wavelength was analysed and correlate with the actual TSS value using multivariate analysis. A regression value of 0.85 for calibration set was found from the analysis, which explained that there was a high correlation between the wavelength and TSS. Stepwise Discriminant analysis method was used to find the significant wavelength that can be used to determine the maturity stage in real-time system. Ten wavelength points were selected and verified on the testing set. The discriminant model can be accurately determined the maturity stage with 85.0% accuracy.
      5  27