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@article{196652,
        author = {Suneeta Devi and Dr.Rohit Kumar Singhal},
        title = {An Efficient Transfer Learning Framework for Real-Time Apple Disease Classification Using Deep Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3382-3400},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196652},
        abstract = {Apples are among the most economically significant fruits globally. However, their quality and yield are frequently compromised by various pathogens. Traditional manual inspection by farmers is subjective, time-consuming, and prone to error. With the advent of Computer Vision and Deep Learning, automated disease detection has become a viable solution to ensure food security and optimize agricultural output. This study presents a transfer learning-based framework for automated classification of apple fruit diseases using deep convolutional neural networks. A custom dataset comprising 616 images across seven classes was developed under real-world conditions. Five pre-trained architectures MobileNetV2, ResNet50, EfficientNetB3, VGG16, and InceptionV3 were fine-tuned and evaluated. Experimental results demonstrate that MobileNetV2 achieved the best trade-off between accuracy and computational efficiency, attaining a peak validation accuracy of 68.03% and superior ROC-AUC performance (0.915 average). The study highlights that lightweight architectures outperform deeper models in small dataset scenarios. The proposed system offers a scalable and practical solution for real-time agricultural disease diagnosis.},
        keywords = {},
        month = {April},
        }