This document summarizes a project that uses deep learning techniques to classify images of fruits and vegetables and estimate their calorie content. A convolutional neural network model achieves 75% accuracy classifying 43 types of fruits and vegetables from images. Users can upload images to a web application that will predict the item and retrieve its calorie information from online sources to display in the application. The project aims to help people track their daily food and calorie intake automatically using images of what they eat.

2. Abstract
This project is about Fruits-Vegetable classification. It is a simple
web application that every user can use it. User need to upload the Image of
any fruit or vegetable. Our system will automatically classify the Image and it
will give you the prediction about the name of fruit or vegetable, and now we
have added one another module which will give you the calories of the
predicted object. This is web application, so user can directly use it in any
browser.
Individuals care about the types of fruit they are eating and the
nutrients it contains because eating fruits and vegetables is an essential part
of leading a healthy diet. In this paper we introduce an automatic way for
detecting and recognizing the fruits in an image in order to enable keeping
track of daily intake automatically using images taken by the user.

3. Abstract
The proposed method uses state of the art deep-learning techniques
for feature extraction and classification. Deep learning methods, especially
convolutional neural networks, have been widely used for a variety of
classification problems and have achieved promising results. Our trained
model has achieved an accuracy of 75% in the task of classification of 43
different types of fruit. The similar methods have achieved up to 70% with
fewer classes

4. Problem Identification
In the existing system are done only manually but in proposed
system we have to computerize using this application.
No such application is in practice nowadays. If we want to
explain a balanced diet to someone, we have to explain it verbally. Thus,
it is not possible to say the right balanced diet for everyone. One is right
and the other is wrong that we giving balanced diet. So this kind of
application is very necessary nowadays.

5. Work Plan
Many health conscious individuals keep track of what they are eating
and how many calories they consume daily. But knowing which food contain
how many calories is not easy for humans. An automated food recognition
system could do the job for them,
just by taking a picture of it. People who want to know how many calories they
are consuming, can take a picture of their food and find out.
In this paper, we used a Convolutional Neural Network, one of the
most widely used Deep learning methods, for feature learning and classifying
the images. The model achieved a top-5 accuracy of 75% and a top-1accuracy
of 45%. The results are highly affected by the quality of the image and the
number of objects it contains. We predict
that by using the pictures that only contain the user’s meal, we can achieve a
higher accuracy up to 95%. In the future, we plan to extend our work and
develop are cognition system that can recognize all types of edible and
drinkable object.

6. Data / Sample Collection
Keras We are using for deep learning tasks like
creating model, predicting the object
etc.
Pillow Pillow we are using for preprocessing the
images of our dataset.
Streamit It is backend framework for developing
the web application.
Beautifulsoup,
Requests
We are using it for scraping the calories
from the internet for the predicted
object.
Numpy We are using it for the Image matrix
handling.

7. Methodology
In this we are going to see how our web-application is working. We
have divided our modules so our task is going to be easy. Our frontend-
backend will be handled by the Streamlit. As a normal user, user will visit our
application by URL. There will be upload button so user can upload the image.
After the uploading the Image our system will do the task automatically.
 User, will upload the Image. That image will be stored into the local
system.
 Now pillow will resize the image according to our model shape, it will
convert into vector.
 Now this vector will be passed to our model, our model will classify the
class of category.
 We will get the ID of category, now we need to map the labels according
to the ID.
 Now our system will do web-scrap the calories for predicted object. Our
application will display the Result and Calories into our application.

8. Literature Survey
[1] D. Pem and R. Jeewon, “Fruit and vegetable intake: benefts and
progress of nutrition education interventions- narrative review article,”
Iranian Journal of Public Health, vol. 44, no. 10, pp. 1309–1321, 2015.
[2] R. C. Fierascu, E. Sieniawska, A. Ortan, I. Fierascu, and J. Xiao, “Fruits
by-products - a source of valuable active principles. a short review,”
Frontiers in Bioengineering and Biotechnology, vol. 8, pp. 319–328, 2020.
[3] S. R. Dubey and A. S. Jalal, “Species and variety detection of fruits and
vegetables from images,” International Journal of Applied Pattern
Recognition, vol. 1, pp. 108–126, 2013.
[4] R. A. A. Al-Fallujah, “Color, shape, and texture based fruit recognition
system,” International Journal of Advanced Research in Computer
Engineering & Technology, vol. 5, pp. 2108–2112, 2016

9. Literature Survey Cont..
[5] S. Arivazhagan, S. Newlin, N. Selva, and G. Lakshmanan, “Fruit
recognition using color and texture features,” Journal of Emerging Trends in
Computing and Information Sciences, vol. 1, no. 2, pp. 90–94, 2010.
[6] P. S. Dutch and K. Jayasimha, “Intra class vegetable recognition system
using deep learning,” in Proceedings of the International Conference on
Intelligent Computing and Control Systems (ICICCS), pp. 602–606,
Maisamaguda, India, June

10. Hardware Requirements
Operating system : Windows 7 & Advanced
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Monitor : 15 VGA Colour.
Ram : 512 Mb.

11. Software Requirements
Operating system : - Windows 10
Coding Language :- Python 3.8
Back-End :- Stream it