Vegetable Image Classification Using CNN (Deep Learning)

In this project, I developed a Convolutional Neural Network (CNN) model using Python and TensorFlow/Keras to classify vegetable images into multiple categories.

The dataset contains thousands of images divided into 15 different classes. Images were preprocessed and resized before being fed into the model. An ImageDataGenerator was used for normalization and efficient data loading.

The CNN architecture consists of multiple Convolutional and MaxPooling layers to extract important visual features, followed by Dense layers for accurate classification.

The model was trained, validated, and tested using separate datasets to ensure good performance and generalization.

Tools & Technologies

Python

TensorFlow / Keras

CNN (Convolutional Neural Networks)

Image Processing

Jupyter Notebook

What the Model Does

Classifies vegetable images automatically

Learns visual features from images using deep learning

Can be used in smart agriculture or food recognition systems