The identification of fruit diseases is significantly enhanced through the application of Artificial Intelligence (AI) techniques in agriculture, enabling early detection and effective management strategies. This advancement is crucial for improving the quality of produce to meet global demands, ultimately benefiting the growing human population by mitigating the spread of plant diseases. The study used a robust dataset comprising 5,800 images of dragon fruit, categorized into fresh and defective types, sourced from reliable repositories. A comprehensive methodology was employed, consisting of six phases: dataset acquisition, pre-processing, model development, model evaluation, model complexity analysis, and the implementation of a web application. Various deep learning frameworks, including VGG-16, MobileNetV2, and DeiT, were used for the classification of dragon fruit diseases, with the VGG16 model achieving the highest accuracy of 99.71%. This study not only demonstrates the effectiveness of deep learning models in disease detection but also contributes to the development of automated tools for quality control in the fruit industry, thereby enhancing agricultural productivity and food security.