https://github.com/sayamalt/fruits-and-vegetables-image-recognition

Successfully established a deep learning model which can accurately detect and recognize the images of a wide variety of fruits and vegetables.
https://github.com/sayamalt/fruits-and-vegetables-image-recognition

computer-vision convolutional-neural-networks image-processing multiclass-image-classification opencv

Last synced: 4 months ago
JSON representation

Successfully established a deep learning model which can accurately detect and recognize the images of a wide variety of fruits and vegetables.

• Host: GitHub
• URL: https://github.com/sayamalt/fruits-and-vegetables-image-recognition
• Owner: SayamAlt
• Created: 2022-11-13T12:03:26.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-11-13T12:29:56.000Z (almost 3 years ago)
• Last Synced: 2024-12-28T08:09:54.540Z (11 months ago)
• Topics: computer-vision, convolutional-neural-networks, image-processing, multiclass-image-classification, opencv
• Language: Jupyter Notebook
• Homepage:
• Size: 84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Fruits-and-Vegtables-Image-Recognition

Successfully established a deep learning model which can detect and recognize the images of a wide variety of fruits and vegetables.

![Fruits and Vegetables Classification](https://i.ytimg.com/vi/w5T86Z3lod0/maxresdefault.jpg)

![Fruits and Vegetables Classification](https://miro.medium.com/max/687/1*YAHQ9G7GDBGuPuF32GPpoA.png)

## Context

The dataset contains images of the following food items:

    fruits- banana, apple, pear, grapes, orange, kiwi, watermelon, pomegranate, pineapple, mango.

    vegetables- cucumber, carrot, capsicum, onion, potato, lemon, tomato, raddish, beetroot, cabbage, lettuce, spinach, soy bean, cauliflower, bell pepper, chilli pepper, turnip, corn, sweetcorn, sweet potato, paprika, jalepeño, ginger, garlic, peas, eggplant.

## Content

The dataset contains three folders:

    train (100 images each)

    test (10 images each)

    validation (10 images each)

Each of the above folders contains subfolders for different fruits and vegetables wherein the images for respective food items are present.

## Python Libraries Used



  
Keras

  
Tensorflow

  
Matplotlib



## Data Collection

The images in this dataset were scraped from Bing Image Search website.

## Inspiration

The main idea was to build an application which recognizes the food item(s) from a captured photo and gives its user distinct recipes that can be developed using the food item(s).