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  • Open Access

    ARTICLE

    Lightweight YOLOv5 with ShuffleNetV2 for Rice Disease Detection in Edge Computing

    Qingtao Meng, Sang-Hyun Lee*

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069970 - 10 November 2025

    Abstract This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the… More >

  • Open Access

    ARTICLE

    Designing and Evaluating a Collaborative Knowledge Management Framework for Leaf Disease Detection

    Komal Bashir1,*, Mariam Rehman2, Afnan Bashir3, Faria Kanwal1

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 751-777, 2022, DOI:10.32604/csse.2022.022247 - 04 January 2022

    Abstract Knowledge Management (KM) has become a dynamic concept for inquiry in research. The management of knowledge from multiple sources requires a systematic approach that can facilitate capturing all important aspects related to a particular discipline, several KM frameworks have been designed to serve this purpose. This research aims to propose a Collaborative Knowledge Management (CKM) Framework that bridges gaps and overcomes weaknesses in existing frameworks. The paper also validates the framework by evaluating its effectiveness for the agriculture sector of Pakistan. A software LCWU aKMS was developed which serves as a practical implementation of the… More >

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