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  1. Fulltext Linkage of SSR markers with rice blast resistance and development of partial resistant advanced lines of rice (Oryza sativa) through marker-assisted selection
    Nihad SAI, Hasan MK, Kabir A, Hasan MA, Bhuiyan MR, Yusop MR, et al.
    Physiol Mol Biol Plants, 2022 Jan;28(1):153-169.
    PMID: 35221577 DOI: 10.1007/s12298-022-01141-3
    Rice blast disease is one of the major bottlenecks of rice production in the world including Bangladesh. To develop blast resistant lines, a cross was made between a high yielding but blast susceptible variety MR263 and a blast resistant variety Pongsu seribu 2. Marker-assisted backcross breeding was followed to develop F1, BC1F1, BC2F1, BC2F2, BC2F3, BC2F4 and BC2F5 population. DNA markers i.e., RM206, RM1359 and RM8225 closely linked to Pb1, pi21 and Piz blast resistant genes, respectively and marker RM276 linked to panicle blast resistant QTL (qPbj-6.1) were used in foreground selection. Calculated chi-square (χ2) value of phenotypic and genotypic segregation data of BC2F1 population followed goodness of fit to the expected ratio (1:1) (phenotypic data χ2 = 1.08, p = 0.701; genotypic data χ2 = range from 0.33 to 3.00, p = 0.08-0.56) and it indicates that the inheritance pattern of blast resistance was followed by a single gene model. Eighty-nine advanced lines of BC2F5 population were developed and out of them, 58 lines contained Piz, Pb1, pi21, and qPbj-6.1 while 31 lines contained Piz, Pb1, and QTL qPbj-6.1. Marker-trait association analysis revealed that molecular markers i.e., RM206, RM276, and RM8225 were tightly linked with blast resistance, and each marker was explained by 33.33% phenotypic variation (resistance reaction). Morphological and pathogenicity performance of advanced lines was better compared to the recurrent parent. Developed blast resistance advanced lines could be used as donors or blast resistant variety for the management of devastating rice blast disease.

    SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12298-022-01141-3.

  2. Fulltext Crowd density estimation using deep learning for Hajj pilgrimage video analytics
    Bhuiyan MR, Abdullah DJ, Hashim DN, Farid FA, Uddin DJ, Abdullah N, et al.
    F1000Res, 2021;10:1190.
    PMID: 35136582 DOI: 10.12688/f1000research.73156.2
    BACKGROUND: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density.

    METHODS: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd).

    RESULTS: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement).

    CONCLUSIONS: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

  3. Fulltext A deep crowd density classification model for Hajj pilgrimage using fully convolutional neural network
    Bhuiyan MR, Abdullah J, Hashim N, Al Farid F, Ahsanul Haque M, Uddin J, et al.
    PeerJ Comput Sci, 2022;8:e895.
    PMID: 35494812 DOI: 10.7717/peerj-cs.895
    This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully convolutional neural network (FCNN)-based method to monitor crowd analysis, especially for the classification of crowd density. This study aims to address the current technological challenges faced in video analysis in a scenario where the movement of large numbers of pilgrims with densities ranging between 7 and 8 per square meter. To address this challenge, this study aims to develop a new dataset based on the Hajj pilgrimage scenario. To validate the proposed method, the proposed model is compared with existing models using existing datasets. The proposed FCNN based method achieved a final accuracy of 100%, 98%, and 98.16% on the proposed dataset, the UCSD dataset, and the JHU-CROWD dataset, respectively. Additionally, The ResNet based method obtained final accuracy of 97%, 89%, and 97% for the proposed dataset, UCSD dataset, and JHU-CROWD dataset, respectively. The proposed Hajj-Crowd-2021 crowd analysis dataset and the model outperformed the other state-of-the-art datasets and models in most cases.
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