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Effective monitoring of plant health is vital for ensuring agricultural productivity and economic stability, particularly in regions aligned with Saudi Arabia’s Vision 2030, which emphasizes technological innovation and sustainability. This study leverages advanced Convolutional Neural Networks (CNNs) integrated with real-time satellite data to detect and classify crop diseases with unprecedented accuracy. By incorporating up-to-date statistics and global case studies from 2023 to 2024, especially from regions with similar climatic conditions such as the Middle East and Africa, this research underscores the technological advancements and market trends shaping sustainable agriculture. Detailed financial projections demonstrate significant cost savings, carbon reduction, and robust ROI across various scenarios. The alignment with Vision 2030 is deepened by illustrating how EcoCropAI fosters economic diversification, job creation in high-tech sectors, and environmental sustainability. Enhanced literature reviews, technical methodologies, and global scalability strategies position this research as a leader in sustainable agricultural technology. Advanced data visualizations and discussions on ethical and social impacts further solidify EcoCropAI’s potential, supported by third-party validations from leading academic and governmental institutions. This comprehensive approach ensures that EcoCropAI stands out at the ICBTOxford 2024 event, offering a robust solution for global food security and sustainable farming practices.

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A Novel Model for Automatic Crop Disease Detection Using Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Nader Alkhater,
  • Layla Alhalwachi,
  • Moheeb Alwarsh,
  • Muaeyd Aldhamin,
  • Nawal Albalawi,
  • Amina alobaid

摘要

Effective monitoring of plant health is vital for ensuring agricultural productivity and economic stability, particularly in regions aligned with Saudi Arabia’s Vision 2030, which emphasizes technological innovation and sustainability. This study leverages advanced Convolutional Neural Networks (CNNs) integrated with real-time satellite data to detect and classify crop diseases with unprecedented accuracy. By incorporating up-to-date statistics and global case studies from 2023 to 2024, especially from regions with similar climatic conditions such as the Middle East and Africa, this research underscores the technological advancements and market trends shaping sustainable agriculture. Detailed financial projections demonstrate significant cost savings, carbon reduction, and robust ROI across various scenarios. The alignment with Vision 2030 is deepened by illustrating how EcoCropAI fosters economic diversification, job creation in high-tech sectors, and environmental sustainability. Enhanced literature reviews, technical methodologies, and global scalability strategies position this research as a leader in sustainable agricultural technology. Advanced data visualizations and discussions on ethical and social impacts further solidify EcoCropAI’s potential, supported by third-party validations from leading academic and governmental institutions. This comprehensive approach ensures that EcoCropAI stands out at the ICBTOxford 2024 event, offering a robust solution for global food security and sustainable farming practices.