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@article{176175,
        author = {Kavita R. Moranij and Dr. D. S. Waghole},
        title = {Plant Disease Recognition From Fruit Using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5447-5453},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176175},
        abstract = {In India's diverse range of crops, fruits play a crucial role in generating significant revenue for farmers. Among these fruits, grapes are extensively grown. However, grape plants are susceptible to various diseases affecting their fruits, stems, and leaves, ultimately impacting their yield. To address this issue, early detection and effective treatment of these diseases are essential to ensure food safety. This study focuses on analyzing different methods for diagnosing and classifying diseases that affect grapevines, with particular emphasis on grape fruit diseases. Monitoring the condition of grape fruits provides valuable insights into the overall health of the grape plants. The research aims to provide a comprehensive overview of techniques used for identifying and categorizing these diseases. Automated disease detection algorithms are proposed to enhance diagnosis accuracy and enable timely control actions. Image processing, a widely used method, is endorsed for fruit disease identification and classification in plants. In this research, diseases infecting grape fruits, such as fungi, viruses, and bacteria, will be subjected to automated disease detection using image processing techniques.},
        keywords = {Grape fruit diseases, deep learning, image processing, disease detection, classification, Google Net, automated diagnosis, feature extraction, agricultural technology, crop health, plant pathology.},
        month = {April},
        }