Latifah Munirah Kamarudin
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Preferred name
Latifah Munirah Kamarudin
Official Name
Kamarudin, Latifah Munirah
Alternative Name
Kamarudin, Latifah Munirah
Kamarudin, Latifah M.
Kamarudin, L. M.
Kamarudin, Munirah L.
Kamarudin, L.
Main Affiliation
Scopus Author ID
57192974774
Researcher ID
G-8267-2016

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  • Publication
    3D grape bunch model reconstruction from 2D images
    ( 2023)
    Woo Yan San
    ;
    Zhuguang Li
    ;
    Tamura Shun
    ;
    Buayai Prawit
    ;
    Nishizaki Hiromitsu
    ;
    Makino Koji
    ;
    Latifah Munirah Kamarudin
    ;
    Xiaoyang Mao
    A crucial step in the production of table grapes is berry thinning. This is because the market value of table grape production is significantly influenced by bunch compactness, bunch form and berry size, all of which are primarily regulated by this task. Grape farmers must count the number of berries in the working bunch and decide which berry should be eliminated during thinning, a process requiring extensive viticultural knowledge. However, the use of 2D pictures for automatic berry counting and identifying the berries to be removed has limitations, as the number of visible berries might vary greatly depending on the direction of view. In addition, it is extremely important to understand the 3D structure of a bunch when considering future automation via robotics. For the reasons stated, obtaining a field-applicable 3D grape bunch model is needed. Thus, the contribution of this study is a novel technology for reconstructing a 3D model of a grape bunch with uniquely identified berries from 2D images captured in the real grape field environment.
      1  21
  • Publication
    Integration of dual band radio waves and ensemble-based approach for rice moisture content determination and localisation
    (Elsevier, 2024-09)
    Noraini Azmi
    ;
    Latifah Munirah Kamarudin
    ;
    Ahmad Shakaff Ali Yeon
    ;
    Ammar Zakaria
    ;
    Hiromitsu Nishizaki
    ;
    Xiaoyang Mao
    ;
    Latifah Mohamed
    ;
    Syed Muhammad Mamduh Syed Zakaria
    ;
    Mohd Hafiz Fazalul Rahiman
    Maintaining optimal moisture content in grain storage is critical to ensuring adequate supply throughout the year, but it presents a significant challenge. Current moisture measurement methods often necessitate sophisticated and costly equipment. This paper introduces an approach employing real-time rice moisture content determination and detection of spoilage (specifically wet spots) within a storage facility achieved through the utilisation of radio waves operating at 2.4 GHz and 868 MHz, along with an ensemble-based machine learning algorithm. Experimental samples spanning from 12% to 30% moisture levels were collected, then subjected to pre-processing, and subsequently employed to train the Ensemble-based Rice Moisture Content and Localisation (eRMCL) algorithm. The eRMCL produced an effective prediction of both rice moisture content and the localisation of wet spots within the grain storage unit. The results show that compared to support vector machine, random forest, and machine learning methods, the eRMCL algorithm had the best performance metrics, with an accuracy of 94.8% in predicting the moisture content and location of spoilage in storage. The measurement of moisture content and the identification of wet spots in rice storage using the dual frequency wave approach were found to be more accurate than with a single frequency band. Thus, the dual frequency band is a novel method for the determination of the moisture content of stored rice and the localisation of the spoilage area.