Fruits Classification Using a Custom Dataset and Ensemble CNN Models with Real-Time Web-Based Deployment

Abstract—The advancement of deep learning has transformed
many fields, including agriculture, where automation and intelligent systems are becoming increasingly important. One
important task is fruit sorting and identification. In this study,
we developed a fruit classification system using Convolutional
Neural Network (CNN) models. Instead of relying on an existing
dataset, we created our own dataset to better represent real
world conditions. A total of 3,200 images were collected across 32
fruit classes under different lighting, backgrounds, and camera
angles. The dataset includes both commonly consumed fruits
and regionally important varieties such as bael, monkey jack,
and wood apple that are not present in most public fruit
datasets, making it more regionally relevant. Before training,
the dataset was carefully prepared by fixing rotation issues,
removing duplicate and corrupted files, and resizing all images
to a standard format. We also applied simple data augmentation
techniques such as flipping and brightness adjustment to help
the models learn better. Several CNN architectures were tested,
including EfficientNet, MobileNet, DenseNet, ResNet50, VGG19,
InceptionV3, and Xception.