<![CDATA[This study focuses on the classification of diseases in apple plants using deep learning methods, particularly Convolutional Neural Networks (CNN). A primary challenge in agricultural management is the early detection of plant diseases, as failure to identify them promptly can lead to significant losses in yield. In this study, various CNN methods were explored to enhance the accuracy of disease detection and computational efficiency. Data were collected from relevant scientific journals, and a literature review was conducted on five main journals that implemented CNN techniques and hybrid methods. The research findings indicate that data preprocessing techniques, such as data augmentation and image segmentation, play a critical role in improving model performance. Hybrid models that combine CNN with other methods, such as RNN, also showed improvements in accuracy and real-time detection capabilities. In conclusion, the implementation of CNN methods tailored to specific needs, combined with appropriate data preprocessing, can provide effective solutions for the rapid and accurate detection and classification of plant diseases.]]>
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