Latifah Munirah Kamarudin
Thumbnail Image
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

Research Output

Filters

2
2
2
Reset filters

Settings
Now showing 1 - 2 of 2
  • Publication
    3D grape bunch model reconstruction from 2D images
    ( 2023-12-01)
    Woo Y.S.
    ;
    Li Z.
    ;
    Tamura S.
    ;
    Buayai P.
    ;
    Nishizaki H.
    ;
    Makino K.
    ;
    Latifah Munirah Kamarudin
    ;
    Mao X.
    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
  • Publication
    End-to-end lightweight berry number prediction for supporting table grape cultivation
    ( 2023-10-01)
    Woo Y.S.
    ;
    Buayai P.
    ;
    Nishizaki H.
    ;
    Makino K.
    ;
    Latifah Munirah Kamarudin
    ;
    Mao X.
    The advent of smart agriculture has revolutionized and streamlined various manual tasks in grape cultivation, one of which is berry thinning. This task necessitates experienced farmers to selectively remove a specific number of berries from the working bunch, as guided by the remaining number of berries in the bunch. In response, this paper introduces a novel real-time edge computing application that automates the process of counting the berries in a working bunch using a single 2D image. The proposed application employs YOLOv5-based object detection techniques (Jocher, 2021) to distinguish each working bunch and the visible and slightly occluded berries contained therein. The key contribution of this paper is to accurately predict the number of berries in the whole bunches including those not visible in a 2D image by harnessing the output from object detection to devise features based solely on bounding box information. In addition, the feature set is optimized by employing a wrapper feature selection method (Kohavi & John, 1997), in consideration of the limitations of edge computing devices. The eight selected features yield a mean absolute error (MAE) of 2.60 berries, tested on a dataset of 26,230 images. Only a slight increase over the initial 19-feature set, which achieved an MAE of 2.42 berries. Furthermore, the proposed approach has been successfully implemented and tested on an Android smartphone, the Sony Xperia 1 III, without the need for an internet connection. The overall computation time per image stands at an average of 0.333 s, confirming its potential for real-world application.
      1