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The rose is one of the ornamental flowers in traditional and cultural ceremonies. It is an economical flower in the international flower markets. The subtropical climate and iron-enriched soil are sufficient for getting higher yields from the rose plant. Different types of diseases affect the rose plant due to the infection of fungus, bacteria, viruses, or other natural and artificial calamities. The diseases are affecting the yield and life span of the plant. The existing techniques are based on the observation of an expert, and the treatment process is also manual. Rose doctor is a proposed methodology that uses a machine learning model to identify the fungal diseases affected by the plant using a Convolutional Neural Network. The training dataset for this model has all the possible images of affected rose plants with various fungal diseases. The data using for test is the image of the affected plant. In addition to the images, the environmental and climatic conditions of the rose plant were also analysed. Rose doctor is giving around 90% accuracy in its prediction results. It is a novel methodology for predicting plant diseases without human support.

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Rose Doctor: Identification of Fungal Diseases in Rose Plants Using Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • John T. Mesia Dhas,
  • B. Senthilkumaran,
  • K. Chinnathambi,
  • R. Durai Vasanth

摘要

The rose is one of the ornamental flowers in traditional and cultural ceremonies. It is an economical flower in the international flower markets. The subtropical climate and iron-enriched soil are sufficient for getting higher yields from the rose plant. Different types of diseases affect the rose plant due to the infection of fungus, bacteria, viruses, or other natural and artificial calamities. The diseases are affecting the yield and life span of the plant. The existing techniques are based on the observation of an expert, and the treatment process is also manual. Rose doctor is a proposed methodology that uses a machine learning model to identify the fungal diseases affected by the plant using a Convolutional Neural Network. The training dataset for this model has all the possible images of affected rose plants with various fungal diseases. The data using for test is the image of the affected plant. In addition to the images, the environmental and climatic conditions of the rose plant were also analysed. Rose doctor is giving around 90% accuracy in its prediction results. It is a novel methodology for predicting plant diseases without human support.