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
    Recent technology for food and beverage quality assessment : A review
    ( 2022-06)
    Wei Keong Tan
    ;
    Zulkifli Husin
    ;
    Muhammad Luqman Yasruddin
    ;
    Muhammad Amir Hakim Ismail
    Food and beverage assessment is an evaluation method used to measure the strengths and weaknesses of a food and beverage system to make improvements. These assessments had become crucial, especially in the issues of adulteration, replacement, and contamination that happened in artificial adjustment relating to the quality, weight and volume. Thus, this review will examine and describe features recently applied in image, odour, taste and electromagnetic, relevant to the food and beverages assessment. This review will also compare and discuss each technique and provides suggestions based on the current technology. This review will deliberate technology integration and the involvement of deep learning to enable several types of current technologies, such as imaging, odour and taste senses, and electromagnetic sensing, to be used in food evaluation applications for inspection and packaging.
  • 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.