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<p>Agriculture is vital to India’s economy, employing a large portion of the population, yet it faces significant challenges, such as crop diseases that cause substantial financial losses. Advanced technologies, including the Internet of Things (IoT) and cloud computing, offer effective solutions to these challenges. This study proposes a Hybrid Weighted Particle Swarm Optimization–based Faster Mask RCNN (HWPSO-FMRCNN) framework to improve the accuracy and efficiency of plant disease detection while enabling secure cloud-based analysis. Existing methods often suffer from limitations in accuracy, computational efficiency, and data security, which can hinder timely and effective plant disease management in agricultural practices. The proposed approach combines the optimization capability of weighted PSO with the feature extraction and segmentation strengths of FMRCNN for enhanced disease classification. The hybrid deep learning model analyses real-time plant images captured by IoT-enabled image acquisition devices. Cloud infrastructure provides secure storage and large-scale analysis, ensuring scalability and accessibility for farmers. By refining FMRCNN hyperparameters through HWPSO, the model achieves superior detection performance. Experimental results show that HWPSO-FMRCNN outperforms existing approaches, achieving accuracy of 98.3%, precision of 96.4%, recall of 99.9%, and F1-score of 99.1%, improving over the best baseline by 3–4% across metrics. The proposed framework demonstrates robustness, scalability, and efficiency, offering a practical tool for farmers to enhance crop management, reduce yield losses, and promote sustainable agricultural practices.</p>

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Optimizing Agricultural Disease Detection with Hybrid Weighted Particle Swarm Optimization-based Faster Mask RCNN for Secure Cloud Analysis and Steady Productivity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月10日

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  • Muneeshwari Paramasivam,
  • Bharathiraja Nagu,
  • Sellakumar Subramanian,
  • Chandramouli Seetharaman,
  • Rupesh Gupta,
  • Anurag Sinha,
  • Ayodele Lasisi,
  • Quadri Noorulhasan Naveed,
  • Ayushman Srivastava

摘要

Agriculture is vital to India’s economy, employing a large portion of the population, yet it faces significant challenges, such as crop diseases that cause substantial financial losses. Advanced technologies, including the Internet of Things (IoT) and cloud computing, offer effective solutions to these challenges. This study proposes a Hybrid Weighted Particle Swarm Optimization–based Faster Mask RCNN (HWPSO-FMRCNN) framework to improve the accuracy and efficiency of plant disease detection while enabling secure cloud-based analysis. Existing methods often suffer from limitations in accuracy, computational efficiency, and data security, which can hinder timely and effective plant disease management in agricultural practices. The proposed approach combines the optimization capability of weighted PSO with the feature extraction and segmentation strengths of FMRCNN for enhanced disease classification. The hybrid deep learning model analyses real-time plant images captured by IoT-enabled image acquisition devices. Cloud infrastructure provides secure storage and large-scale analysis, ensuring scalability and accessibility for farmers. By refining FMRCNN hyperparameters through HWPSO, the model achieves superior detection performance. Experimental results show that HWPSO-FMRCNN outperforms existing approaches, achieving accuracy of 98.3%, precision of 96.4%, recall of 99.9%, and F1-score of 99.1%, improving over the best baseline by 3–4% across metrics. The proposed framework demonstrates robustness, scalability, and efficiency, offering a practical tool for farmers to enhance crop management, reduce yield losses, and promote sustainable agricultural practices.