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

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  • 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.
  • 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.
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