Fruit Classification Model Selection and Performance Analysis Flowchart

This flowchart outlines the process of developing a fruit classification model, from data collection to web application deployment. Starting with a dataset of fruit images, data preprocessing is conducted before selecting models like VGG19, Resnet152V2, MobileNet, DenseNet201, and others. The dataset is then used for training, followed by performance analysis using metrics such as accuracy, F1-score, recall, and support. The best-performing model, DenseNet201, is proposed for deployment in a web-based application where users can upload fruit images for classification.