This study aims to develop a deep learning-based model for accurate fruit disease classification using advanced architectures like ResNet50 and VGG19. The dataset includes images of fruits such as grapes, mangoes, lemons, pomegranates, and guavas, with both healthy and diseased categories. Diseases like black rot in grapes and bacterial canker in mangoes are considered for classification. The objective is to build a model capable of accurately identifying these diseases to aid in early identification and control. The approach begins with pre-processing the photographs using standardisation, resizing, and data augmentation techniques including flipping, zooming, and rotation to strengthen the model. By enhancing picture contrast with Contrast Limited Adaptive Histogram Equalisation (CLAHE), important illness characteristics can be better highlighted. We train and evaluate deep learning models by dividing the dataset into three parts: training, validation, and test. The ratio of the parts is 80:10:10. According on the results, the ResNet50 model outperforms all other accuracy at 99.77%.