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Paddy leaf disease greatly affects and limits the rice production and consequently, world food security. In this paper, Deep transfer learning and ConvNeXT model is used to develop an intelligent real-time paddy crop disease diagnostic system. By fine tuning a cumulative and expanded paddy leaf disease image dataset with a pre-trained ConvNeXT model, the system achieved a high level classification performance with an accuracy 98.32%, precision level 93.00%, recall level 94.00%, and an F1-score of 94.66%. This research work presents the computational efficiency, data variability robustness, and deploy ability of ConvNeXT model to be used in agricultural fields. Application of the model was designed to eqiped farmers with real-time diagnostic feedback, thus limiting losses through crops while ensuring sustainable agricultural practices.

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Improved Paddy Leaf Disease Detection Model Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Achintya Gour,
  • Priyanka Goyal,
  • Vimlesh Kumar Ray

摘要

Paddy leaf disease greatly affects and limits the rice production and consequently, world food security. In this paper, Deep transfer learning and ConvNeXT model is used to develop an intelligent real-time paddy crop disease diagnostic system. By fine tuning a cumulative and expanded paddy leaf disease image dataset with a pre-trained ConvNeXT model, the system achieved a high level classification performance with an accuracy 98.32%, precision level 93.00%, recall level 94.00%, and an F1-score of 94.66%. This research work presents the computational efficiency, data variability robustness, and deploy ability of ConvNeXT model to be used in agricultural fields. Application of the model was designed to eqiped farmers with real-time diagnostic feedback, thus limiting losses through crops while ensuring sustainable agricultural practices.