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A novel approach for classifying leaf images from plants within the Rosaceae family, especially strawberries, apples, and cherries, has been presented in this study. We have deployed our models by utilizing leaf morphology for classification. The distinctive characteristics of these species has been highlighted throughout our study. Through data preprocessing, model training using convolutional neural networks (CNNs) along with integrated mechanisms and evaluations based on evaluation metrics, we have trained our models. We have integrated disease detection capabilities that can identify common leaf pathologies, hence enhancing the practical application of this model in agricultural and ecological monitoring. The findings have contributed to a deeper understanding of the significance of leaf morphology in ecological studies.

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Automated Identification and Disease Detection of Rosaceae Species Using Deep Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Sneha Upadhyay,
  • Dibyasree Mukherjee,
  • Lipakshi Shaw,
  • Rupanwita Das Mahapatra,
  • Jhilam Mukherjee

摘要

A novel approach for classifying leaf images from plants within the Rosaceae family, especially strawberries, apples, and cherries, has been presented in this study. We have deployed our models by utilizing leaf morphology for classification. The distinctive characteristics of these species has been highlighted throughout our study. Through data preprocessing, model training using convolutional neural networks (CNNs) along with integrated mechanisms and evaluations based on evaluation metrics, we have trained our models. We have integrated disease detection capabilities that can identify common leaf pathologies, hence enhancing the practical application of this model in agricultural and ecological monitoring. The findings have contributed to a deeper understanding of the significance of leaf morphology in ecological studies.