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This research examines a novel and comprehensive approach of plant diseases diagnosis which integrates advanced image processing and machine learning. The principal aim is to enhance the precision and efficacy of plant diseases diagnosis, which will translate to better crop management and agricultural practices. To enhance the accuracy of detecting plant infections, this study explores novel approaches that seamlessly integrate machine learning with image processing. Additionally, it presents a comprehensive review of existing research in this domain. By utilizing this comprehensive system, the study aims to advance the development of plant disease detection technologies and provide a strong foundation for decision-making and improved management of agricultural resources.

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Harmonizing Image Processing and Machine Learning for Enhanced Plant Disease Detection: An Integrated Framework

  • 会议论文

  • 发表日期：

    2025年

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  • Kulvinder Singh,
  • Sidharth Aggarwal,
  • Aryan,
  • Akashat Srivastava,
  • Kshitij Vats,
  • Himanshu Gera

摘要

This research examines a novel and comprehensive approach of plant diseases diagnosis which integrates advanced image processing and machine learning. The principal aim is to enhance the precision and efficacy of plant diseases diagnosis, which will translate to better crop management and agricultural practices. To enhance the accuracy of detecting plant infections, this study explores novel approaches that seamlessly integrate machine learning with image processing. Additionally, it presents a comprehensive review of existing research in this domain. By utilizing this comprehensive system, the study aims to advance the development of plant disease detection technologies and provide a strong foundation for decision-making and improved management of agricultural resources.