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
Main Affiliation
Scopus Author ID
56571962400
Researcher ID
AAE-5507-2020

Research Output
Now showing 1 - 2 of 2
  • 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.
  • Publication
    Deep CNN-based Planthopper classification using a high-density image dataset
    (MDPI, 2023)
    Mohd Firdaus Ibrahim
    ;
    Siti Khairunniza-Bejo
    ;
    Marsyita Hanafi
    ;
    Mahirah Jahari
    ;
    Mohammad Aufa Mhd Bookeri
    ;
    Fathinul Syahir Ahmad Sa'ad
    Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, it is crucial to apply appropriate pesticides specific to the type of pest in the field. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. In this study, a machine vision system was developed using a dataset of 7328 high-density images (1229 pixels per centimetre) of planthoppers collected in the field using sticky light traps. The dataset included four planthopper classes: brown planthopper (BPH), green leafhopper (GLH), white-backed planthopper (WBPH), and zigzag leafhopper (ZIGZAG). Five deep CNN models—ResNet-50, ResNet-101, ResNet-152, VGG-16, and VGG-19—were applied and tuned to classify the planthopper species. The experimental results indicated that the ResNet-50 model performed the best overall, achieving average values of 97.28% for accuracy, 92.05% for precision, 94.47% for recall, and 93.07% for the F1-score. In conclusion, this study successfully classified planthopper classes with excellent performance by utilising deep CNN architectures on a high-density image dataset. This capability has the potential to serve as a tool for classifying and counting planthopper samples collected using light traps.