Zulkifli Husin
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Zulkifli Husin
Official Name
Zulkifli, Husin
Alternative Name
Husin, Zulkifli
Husin, Z.
Husin, Zulkifli Bin
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Scopus Author ID
57201059019
Researcher ID
EXV-4088-2022

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  • Publication
    Feasibility Study of Beef Quality Assessment using Computer Vision and Deep Neural Network (DNN) Algorithm
    ( 2020-08-24)
    Tan Wei Keong
    ;
    Zulkifli Husin
    ;
    Hakim Ismail Muhammad Amir
    The beef quality relies upon the colour score of muscle during the grading stage. Colour scoring to be used in beef grading would be very critical and the current way of identification and determination of the quality of beef is still being done manually and susceptible to human error. The ability to automate the prediction of the beef quality will assist the meat industry through the grading phase to establish the colour score. Therefore, computer vision and deep neural network (DNN) were used to predict the beef quality by determining colour scores of beef muscle. Four hundred of beef rib-eye steaks were chosen and acquired for each image, which is the colour score of beef were assigned by expertise according to the standard colour cards. The image was processed and went through DNN classifier for determining beef quality. The proposed DNN classifier achieved the best performance percentage of 90.0%, showing that the computer vision integrated with the DNN algorithm can deliver an efficient implementation for predicting beef quality using colour scores of beef muscle.
      1
  • Publication
    Automated tomato grading system using Computer Vision (CV) and Deep Neural Network (DNN) Algorithm
    ( 2022-01-01)
    Tan Wei Keong
    ;
    Muhammad Amir Hakim Ismail
    ;
    Zulkifli Husin
    ;
    Muhammad Luqman Yasruddin
    The tomato grading is based on the skin colour at the grading stage. The evaluation of the colour used to classify tomatoes is very important, and the current methods of identifying and determining tomato varieties are still manual and prone to human error. The ability to automate tomato grading helps the food industry determine colour grades during the evaluation phase. Therefore, Computer Vision (CV) and Deep Neural Network (DNN) are utilised to grade tomatoes by determining their maturity colour. Three hundred tomatoes were selected and its maturity level are assigned by expertise. The tomato images are captured, processed and passed to the DNN classifier to determine the tomato grade. The proposed DNN classifier achieved the mAP percentage of 95.52%. This shows that the computer vision built into the DNN algorithm can provide an efficient implementation for predicting tomato grade.
      3  1